{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过降维减少特征数量，以提升模型训练的速度和模型预测的精度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#忽视警告\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Diabetes_binary</th>\n",
       "      <th>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>HeartDiseaseorAttack</th>\n",
       "      <th>PhysActivity</th>\n",
       "      <th>Fruits</th>\n",
       "      <th>...</th>\n",
       "      <th>AnyHealthcare</th>\n",
       "      <th>NoDocbcCost</th>\n",
       "      <th>GenHlth</th>\n",
       "      <th>MentHlth</th>\n",
       "      <th>PhysHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98939</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209426</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48298</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153179</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191760</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Diabetes_binary  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
       "98939               0.0     0.0       0.0        1.0  40.0     1.0     0.0   \n",
       "209426              0.0     0.0       1.0        0.0  31.0     0.0     0.0   \n",
       "48298               0.0     0.0       0.0        1.0  21.0     1.0     0.0   \n",
       "153179              0.0     0.0       0.0        1.0  26.0     1.0     0.0   \n",
       "191760              0.0     0.0       0.0        1.0  25.0     0.0     0.0   \n",
       "\n",
       "        HeartDiseaseorAttack  PhysActivity  Fruits  ...  AnyHealthcare  \\\n",
       "98939                    0.0           1.0     0.0  ...            1.0   \n",
       "209426                   0.0           0.0     1.0  ...            1.0   \n",
       "48298                    0.0           1.0     1.0  ...            1.0   \n",
       "153179                   0.0           1.0     1.0  ...            1.0   \n",
       "191760                   0.0           1.0     1.0  ...            1.0   \n",
       "\n",
       "        NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex   Age  \\\n",
       "98939           0.0      4.0       0.0       3.0       1.0  1.0   8.0   \n",
       "209426          1.0      3.0      10.0       7.0       1.0  0.0   4.0   \n",
       "48298           0.0      3.0      10.0       1.0       0.0  1.0   8.0   \n",
       "153179          0.0      1.0       0.0       0.0       0.0  1.0   2.0   \n",
       "191760          0.0      1.0       0.0       0.0       0.0  0.0  10.0   \n",
       "\n",
       "        Education  Income  \n",
       "98939         4.0     3.0  \n",
       "209426        3.0     1.0  \n",
       "48298         4.0     1.0  \n",
       "153179        6.0     8.0  \n",
       "191760        6.0     8.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 使用pandas的read_csv函数读取CSV文件\n",
    "data = pd.read_csv(r'D:\\机器学习实战项目\\疾病分类预测\\diabetes.csv')\n",
    "\n",
    "#随机打乱数据集，防止模型学习到固定的顺序\n",
    "random_seed = 77\n",
    "data = data.sample(frac=1, random_state=random_seed)\n",
    "\n",
    "# 显示前几行数据，以确认数据已正确加载\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 253680 entries, 98939 to 178903\n",
      "Data columns (total 22 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   Diabetes_binary       253680 non-null  float64\n",
      " 1   HighBP                253680 non-null  float64\n",
      " 2   HighChol              253680 non-null  float64\n",
      " 3   CholCheck             253680 non-null  float64\n",
      " 4   BMI                   253680 non-null  float64\n",
      " 5   Smoker                253680 non-null  float64\n",
      " 6   Stroke                253680 non-null  float64\n",
      " 7   HeartDiseaseorAttack  253680 non-null  float64\n",
      " 8   PhysActivity          253680 non-null  float64\n",
      " 9   Fruits                253680 non-null  float64\n",
      " 10  Veggies               253680 non-null  float64\n",
      " 11  HvyAlcoholConsump     253680 non-null  float64\n",
      " 12  AnyHealthcare         253680 non-null  float64\n",
      " 13  NoDocbcCost           253680 non-null  float64\n",
      " 14  GenHlth               253680 non-null  float64\n",
      " 15  MentHlth              253680 non-null  float64\n",
      " 16  PhysHlth              253680 non-null  float64\n",
      " 17  DiffWalk              253680 non-null  float64\n",
      " 18  Sex                   253680 non-null  float64\n",
      " 19  Age                   253680 non-null  float64\n",
      " 20  Education             253680 non-null  float64\n",
      " 21  Income                253680 non-null  float64\n",
      "dtypes: float64(22)\n",
      "memory usage: 44.5 MB\n"
     ]
    }
   ],
   "source": [
    "#数据情况\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FOmM/d9ttt6FevXpYsGABJk+ejNKlS5/VZxVH27Zt8ddff2Hq1Kno0aMHHnnkkSLbmk2bNmHVqlVYsmQJBgwYgHnz5umcuTBn6oMcDkeBNjAjI8O0/0eMGIH+/fvjf//73xnzh+LmRuqsq3OhftPFamsKy4mcTmexFk/OltPp1HlOhw4dTO3A0aNH9UEjF9rFHCtdyLbmhhtuwLPPPov58+cXmNxR5cBms6Fnz57F+k3FHX8CZ5cbt2vXzhS7C50bXyxn256cjdOVgws9h2Osw0eOHIHd7ruIkjFfKE4uf7mos8YeffRR3HrrrUhMTETv3r0v2/jY6XTC7XZfsu9TzrZfyN8XtG3bFuvXr9eLW2PGjNH7r1+/fgDOvy8AgOPHj0NEEBAQgISEBIwbNw5dunS5oPlphQoVdJ8TEBCAF198EV6vFyEhIRg2bBgqVaqEdu3andd3nEs9rFu3LmbPno06depg+PDhAIrefx6PRy8qOxwOfP/99wUO8H344Yfx3HPPndfvyM8qc4E5OTlnHGNdjrOPrLL/lIuVn/mLyzV+M5Gz8NJLLwkAWbNmjen5vLw8CQ0Nld69e5ueP3z4sNx1110SHx8vbrdbKlSoIBMnTpS8vDwRETl69KhUrFhRKlasKEePHtXv27t3r5QqVUoaNGggOTk5IiKSlZUlQUFBsnHjRmnVqpUEBgZKbGysjB07Vn+eAkBGjx5teu67776TNm3aSEhIiAQFBUnz5s3lyy+/LPDb8j8+/vjj0+6TFStWSHp6ugQGBkpYWJh07NhRNmzYoP+elZVV4DMzMjKK/LyPP/5YAMiiRYsK/furr76qP0fFQW37n3/+qeOwdOlSadeuncTGxorL5ZKwsDAJDQ0tEIeMjAypXLmyJCYmitfrFY/HI0lJSTJjxowCcTh+/LjUqFFDbDabuFwu8Xg84nK5THEobB9mZWXp7X///fclLi5ObDabAJDAwEC5//77zxiHChUqyLx584odh8zMTAEgEydOlMmTJ0tQUFCRcRg9erTkrwrqNSNGjJCqVauK2+2WKlWqyHvvvWeKQ/PmzQWAlClTRpxOpwCQtm3b6lg0a9ZM2rVrJyVKlBAAUqpUKYmJiRG73S4AJD4+XgBIlSpVpGrVqvL555+L1+sVm80mkZGRAkBWrVplisWRI0dk1KhREhISorezVKlS4nQ6xWazidvtLnQfOhwOOXnypIiIbNmyRTp27Gh6baVKlYpVJx544IEC+x+A3H777ToWderUEQDi9XoFgAwfPlwmT56sf1P+h8fj0XHYtGmTjp0qIwDE5XKJy+UyxUG9B4DUrl1bKlSoIF6vV7+nT58+ehtnzpypX+tyuSQ0NFSXF/X8kiVLJCMjQ0qXLi0AJDU1VVwuly4L+du648ePy6hRoyQqKkoAiN1uF5vNJna7XTwej9x4442F/t5evXqJiK+ti46OlqioKHG5XGKz2SQsLEw8Hk+Btq5t27YSGhoqAKREiRKSlpZWoK0DIN27d9dtndoPI0eO1Pu1e/fuhW5T69atJTExscB+nThxomnfqd9ns9mkbNmy8t5770mbNm10nTY+bDablCxZUrp16yabNm0SEdHb0bRpU/1b7Ha7OJ1OCQkJkY8++kgAyJ133ikA5Prrr9d1W8Xiqaee0r956dKlUr16dQEgbrdb14sZM2ZI//79pWTJkhISEiLR0dFit9ulffv20qBBA3E4HGK322XSpEkF+qly5cpJeHi4lC1bVlwulzidTilZsqTs37+/QBthfJQtW1b3UxkZGeJ0OsVut+v6PnDgQHnooYd0exAbGysPPvigVKlSRQICAnQ7pfax3W6X0NBQ6dGjh/Tv31/Cw8ML/V4AEhERoeuS1+uVJk2ayOeffy6lSpWSKlWqSKNGjfT+czgccsMNN5jilP/zUlNTTeVrypQpBeqv3W6Xhg0bmtqNDh06FLp9PXv2FABSr149CQoK0rFr166djpnqW/L3pRkZGbos5y9fvXr1kn379unvv+GGGyQgIEDX4ZIlS8qIESPk+PHj0rJlS6lQoUKh25eVlSVPPvmkAJC+ffvKTTfdJNHR0eJ0OiU4OFjCwsLE7XZLSkqKjBs3Tj788EPdhqu2X7XlV111lXi9XgkNDZXg4GAJCgqSPn36yPHjx03tRpkyZQq0G06nU9LT04uMszJlyhRT2+7xeMThcEhQUJAEBAQIAImOjpahQ4dK+fLldf9g3HdBQUHi9XqlRo0apjYyISFB+vXrJ1FRUaZ+JioqShISEsTr9UpMTIwEBATo7fB6vRIcHCwATDmZcduTk5PFbrfr7S1durTePw0bNpSEhASx2+3i9XoL7bMByD333CMAZOXKlXLrrbdKVFSUlChRQhYvXqzblfw52eOPPy6Ary9t3bp1oZ/bt29fASA1atSQ8PBwCQsL020E4MvJdu7cKeXKldP9bPXq1WXgwIECQO6//35p2bKlJCcn689UbV5eXp60bNnS1D+npKRI165dpVatWuL1eiU8PFx69Oihy+w333wjDRo0EK/XK4mJidKuXTspW7asuN1uiY+PlxEjRsjBgwdl1KhR+vnw8HBJSEjQcUhKSir0tzqdTmnfvr28//77kpqaqutdUfXi6NGjuu9xu926/5k5c6bOSx566CEBIA0aNNBtYNmyZXU7p9rzfv36SVJSki6LTqdTbrnlFl1evvvuO0lJSdFl1Gaz6f514MCBMnjw4CJ/U+XKlWX8+PFy66236lygZMmS0qJFC0lNTdX1VPVLxnLk8XgKfGZsbKypbZk3b54AkGHDhkliYqLuv2677TbdN+TPm1S9dDgckpGRIQCkS5cu0rt3b7Hb7dK6dWudr6qHy+WSyMhIadKkSYFtiomJkapVqxa6D1566SX9t9KlS5vqe3R0tLRp00aCgoKKzMMAXy6bkpIiwcHBuv8CoNuUUaNGFfo+m82my8W8efPk8OHD0rBhQ13mg4KCxOl0SkBAgISFhUn79u0lOTlZxzo6OrrAZ9aoUUOPifI/VBkHICEhIfK///1PqlSpove5aifuv/9++e6776RRo0ambVXvz8rK0vlDYY+oqCjdDwO+fEjVy+joaN1uA5D09HTZsGGDbt+Nbad61KxZU/r371/od6m2KTg4WDZt2qT7IDX+iIuL07/J2N+p31yiRAmJiIgQp9Opx3/GPisnJ0fndj/99JPOiQBIYmKiqU42adJEfvzxRxHxjWPLlClToDw2bNhQAEjdunWL3H/XX3+9dO3atdD2JTQ0VB566CHTPICqPwkJCfp1drtdHnjgAQF8/Uz+3Lhr164C+Np3Y47ZsmVLmTx5spQpU0Z/vzGndzgcuo0qrO4a25YqVapIrVq1pEKFCnrf//XXX3LrrbdKfHy8qa4FBweb5gGMZU89HA6HzJ49WwBIUFCQeDweSUtLkzfeeMNU5letWiXr1q2TJk2aiNfrlbi4OLn22mtNn/XJJ5/otsXlcklAQIB4vV6pVq2aNGnSRG+bakenT58ugG9+o2XLluL1esVut0ujRo10bmqz2SQlJUV69Oihc68KFSrImDFjpG7duqacB4B07txZRER27NhR6D4sW7as/pxy5cqJw+HQ31O5cmV54YUXCuQMWVlZOjcODg7WbXRQUJDExcVJfHy8fk9RsVN1dvTo0ab8unTp0jJw4EB5//33JT09XZeJ2NhYCQ0N1e2dGqPnfyxYsEAeeughiYuLE7fbLXFxcZKcnKw/p379+rJp0yZZuHCh1K5du0BOU7t2bZ27qHr07LPPiojI5s2bxW63S2RkpI5H6dKldR02ysjI0O2CsU202WzSpk0bU/9Vv359/Vkqt3E6ndKhQwfJyMgoMje22+2SlJQkN910k2zZssU0Zna5XNKkSROd56g2T32Xx+MpML6w2WwSGhoqU6dOld69e0tYWJgEBASIzWaT+Ph4mTdvnq7DNptNj23UtqgyEBsbK4GBgabfrebO1L87dOhgKtOlSpWSatWq6bnAhg0bSqdOnaRkyZKFjoPUQ/UnAKROnToSFRWl5zfyP0JCQqRjx476348++qjeBuOjdu3autwuXrzYtP/VZ8+fP18OHz6s/6ZyIlX/+vbtK82bNxeXyyWlSpXS758/f77cdddd+jepz1P5p9vt1mNMtX/Dw8NNeacq/9HR0eLxeKR69eoyduxYadWqlbjdbr0tAKRWrVpy/Pjx09ZD41zg2rVrpXz58vr9LpdLYmJiCsylPvXUU6bfZXyoOTzAN/80d+5cqVChgng8HilfvryprVNzgXFxcTofAyBhYWEycOBAOXHihHTq1El/tppvBHx9Y3h4uKkdffvtt/VvmT59ut5fqnwCvrmrhx56SEREfvvtN+nSpYvOIwICAkz5aFH7zPho166d/s4tW7aY8gM13v3kk09M8x5169aVihUrSkBAgM4PgoOD9VygkRofGxnn48aMGSNAwfxAyT+Xevz4cQF8/bzT6RSn0ykOh0MqV66s5/B69+4tHo9Hli9fbsrb1Xzf6tWrTd+hylf+8Z+aN1qyZEmB36XydpUfFEZ9bv79l39+oXfv3hIREVHs/ZefWssojvXr10uzZs10vz9+/Hh54YUXBDg1thQpfM1DRCQxMdFU50RE9u/fL0OHDpXExETdb/Xq1Ut2794tIiInTpyQBx98UGrXrq3nSNPT0+Wjjz7Sn6HKRP6H2obC5tSzs7Nl3LhxkpKSIm63WxITE+W+++7TcyLGbW7fvr189tlnUrduXfF4PJKcnCwvv/xysfaZ3idn82IV/A8//FB2794tu3btkvXr18uAAQPEbrfLsmXL9Gvz8vKkefPmYrPZ5JZbbpFp06bpSa+hQ4fq161evVocDofceeed+rmePXtKQECA/Prrr/q5rKws8Xq9Ur58eenVq5dMmzZNrr76agEgDz74oPlH5Qv0+vXrdUc4fvx4eeyxxyQ5OVk8Ho+uOBs3bpQ77rhDAN/AYc6cOTJnzhzZsWNHkftj+fLl4nQ6pUKFCvL444/L2LFjJTIyUsLDw3XBW7Vqldx///0CQO644w6ZM2eOaT/ld6bFrZMnT+rkScXhqaeeEsA3iFBx6Ny5s3Tv3l0ef/xxqVixoi58LVq0MMVBTeSrhltNDAGQq666SschNzdXWrVqpTuF6OhoqVixotjtdomJidFxmDNnjng8HmncuLEAkGuuuUY3KCtXrtQDwebNm0vnzp11sqvKxMaNG/WCUaVKleSmm26SG2+8Ua6//nq54447ih0HNXFRsWJFSUpKkttuu01vU0BAgEybNk3H4XSLWyVKlJDx48fLk08+KSkpKRIYGCh79uyRkydPSkBAgB4s3HjjjXryNigoSIKDg/XEb/fu3fWCgupQS5cuLZ06ddKx8Hq9Urp0aYmOjpZu3brpxREA8sILL+g68fPPP+tBXeXKlfXnqe81TnrYbDZp3LixzJkzRz/31ltvyY4dOyQmJkZPbqrOWnVaqk48/PDD+n0qIWjYsKHccMMNsnv3btNDJRcqFmqCr1WrVvp3qwmV8PBw3amribro6OgCi1u1atUSADqWbrdbwsLCTHFQ73G5XOL1emXYsGEycuRIXSajo6PlyJEjIiJ6WwDfYqD6zbVr19aJSNeuXSUjI0Mnop06ddLJkkqmVB1TdSIgIEBvo3qdcVKjYsWK4vF49MKwShRERDp37iw2m01PknTo0EEP6qOionRbpyZf1AT7ww8/rCc1RowYYYqD0+nUbZ1KyJ1OpyxZskTHUm1bSkqKbk/KlStX6OJWrVq1dOLkcDjEZrOJx+ORoKAgPRHtdDolLS1Nt3WRkZHSuXNnGTp0qNx///0SHh4uiYmJcuTIER1fNZkVGhoqKSkpEhQUJKGhoXLixAlJSEiQa665Ru9PVbdVXENDQ3Ub27lzZz258tBDD+myFxoaKvXr15eHH35Y101VpwcNGiS33367qa6ofqply5Y6SR06dKjMnDlTunXrJoBvgC7iS1TVe9PS0nR/0a5dO/F6vbqMlihRosCAMT4+Xq677jpTUtuhQwe59dZbTYlxfHy8lCxZUi98qPZUTWKox+TJk011uEKFCroN9Hg84na7xeFwSIUKFUyTqMaE2tiOqLqg9v3o0aP1xHVISIg4HA7TRJVaqFi9erUsW7bM9BscDodUqlRJYmJi9CJIXFycTu46d+4smZmZelvUILVhw4amvjQjI8M0gA0NDdW/12azSZMmTSQvL09yc3P1oq+aMGjTpo04nU5p3bq1OBwOGTdunMyZM0cP7pKTk2XOnDmyatUqnTg6nU6Ji4uTcePGSY0aNXT579atm55YUgv4qk2KjIzU7Vr58uUlLS1NL0Y2aNBAAMjYsWN1uxEYGCjlypXTbZv6LNWehYSE6L40IiJCl30R0Qv/xnipbVQDVuOkQLVq1SQuLk63U2q/qVg1a9ZMRMwTRHFxcTJ16lS5++679XNer1cmTJggY8eO1dusJp6mTZumy7oxJzNuZ0xMjKSkpJgmRxITE+XRRx/VbSAAGTRokGnB4dZbbxWbzSZdu3bVC5BVqlSRjIwMmTp1qjz22GOSk5OjP7Ns2bKmnMzlckmZMmVEROTtt9/Wk7TVqlWTgQMHyt133y0TJkzQ/ajL5ZLg4GC9wAtA5s6da+pz+/fvr3MKl8slNWrU0Lmses+mTZskJydHL2Q0btxY0tLSxO12S6VKlXS7+/jjj8vdd9+t4xMYGCjR0dEyaNAgeeqpp3R/1LJlS5k5c6YMGjRIHA6HREVFSWBgoG6n1D5UbbyqCw6HQ2rXrq1z8vDwcClXrpyEhYXpRQxVbo3tQuvWrWXVqlUya9Ys/VzdunXlqaeekr59+0qFChV0jrh582YdT5Wrq35L5eqq3Lndbj35o9qTHTt26Fxd7eNGjRpJZGSk/req92riTE3QdevWTR5++GHdx8fFxcmoUaNk1qxZMmTIEP3dkZGR0q1bN2nSpIku++XKlZOMjAypVq2afp3H45FSpUqJ1+uVunXr6oFsu3btdJ1WeUObNm3E4XDo1+Wf3FGTtyquAOSVV16Rp59+WgBI5cqVdV8YEBBg2o5rrrlGatasKZGRkXoxze12y80332zqx6+99lqZM2eObNy40bQoEBUVZZoYdDqdMmPGDL0YoLYvODhYx14dzBEaGiphYWG6rVftS40aNfR7jQu5VatW1eVi8ODBOo+vUKGCDBo0SL+uZs2aul6GhISY+gv1Xcb+4Mcff5SaNWvquACQ3r176/xQvU/lPOq5gIAACQgIkKpVq0pQUJDp4Ijw8HBdplwul7Ru3brAApfH45Fy5crJ1KlTpVmzZrpt9Xg8BfKHkiVLSnh4uO6zGjduLIGBgdK9e3dp27at6TclJSVJ48aNpVevXnobKlasKK+88opMmzbNVFYTEhJk3LhxMmPGDL3wZbfbpU6dOiIipnr58MMPy1NPPaXj2KlTJxkzZoxMnDhR17Xhw4fr3K5KlSoSERGhc1W1KGZcQIiKipIdO3ZI79699YRT/rKtfpNxscLlconD4ZDJkyfrBcegoCBTn6MOjgR8YwfVdvbp00eXVZvNJq1atZI6derI4MGDJTAwUOx2e6HzAOHh4brPMeaYSUlJMmHCBF0eAV9u1aFDBz3RCPgOyNi4caN07txZAF9fHBYWJl27dpXx48fr+nfvvffq71m0aJGkpKTocXDz5s11W2rMUdXCHODLP3v37q0nOp1Op1SrVk2mTZumF6LGjRun+8stW7ZIyZIlJSIiQsaOHStPPPGEVKxY0dROp6amFsgzVT4fEBCgY6ryDXVg2Ysvvih2u92UW5UqVUo6d+5sOjAiISFB3G63aQxVvXp1ufbaa3XbFhAQoPeL2q/5c2O1b9REq5oQVts+ZcoUnTOo8tStWzcZPXq0nkyPioqSxx57TGJjY/VvUHMHag5HtZ0qN1b9tsvlkvr16+v2IC4uTs8dqJzAZrOJ1+uVkSNH6s9S29mkSRPdF6empkpaWppMmTJFrr32Wr1gNmzYMF3eVdtbt25dmTJlirRr105/ZnBwsM5dVP1s06aNqV5fddVVUqlSJYmMjNR55/Dhw01zFRkZGaaDw8LDw015Xq1atXRurGIaGhoqFStWNPWDqtypORyn0ymlSpWSnj17yr333isPPPCAlC9fXu939VD9wrBhw/RcoPHv8fHxOpbqb8b8b/jw4bpMqoUWh8Mh//vf/0zttfH9wKkxtvEACaDg4pb6vW3btjUtMLVt21Yee+wxnTMFBARIZmamNGnSxNSWPfTQQzJnzhyddwK+HGfq1KkyZMgQ09hS7a8HHnhAz3eovqlfv36msSUAmTBhgs4DjGMwY9754osv6rwN8PWfxr5KHSzWr18/0/6pUqWKlChRQmw2m1SoUEFq1aplWpC22WxSp04d00EwLpdL552TJk3Sr23ZsqU8/fTTOudRdUnlZ+rzOnXqZKqHDodDKlasqMvI8uXLdfustqVBgwamMW3VqlV1fmDss5OTk3U7oQ4wVnN4gC+Xj4yMlHHjxsmECRP0eEm1vampqZKQkCAhISH64HzVxlauXFn279+vF7c8Ho+cOHFCf3ZYWJiEhIRIhw4ddC7idDpNc1BqPsTY/qv/nzp1qiQnJ0vp0qXlmmuu0b/J5XJJhw4d9MG2gK8fnjNnjgwcOFDcbrc0btxYatSoIdHR0XoudceOHRIfH2/KD9R2xcbGSuPGjeXpp5/W7XxkZKQ8++yz8tRTT0mbNm0E8M0FGm3fvl2Pj41UX5qamipJSUl6fik8PFznB4pxLlWNc9X3h4WFSb169cRut0tgYKCew9u/f7/Ex8dL2bJlpUaNGjJq1Cg9txUUFKTnjRSVX+cf/+Xl5UlCQoJ07dpV8lMHBp6O+tzU1FS9/26//Xax2+16fkHEN998NvsvP7W4lX8Odffu3XLw4EHT50VFRUl4eLjO44xjqnNZ3Pr333+lWrVqur2YMWOGjB8/XurWrSvff/+9iIjs3r1bYmNj5a677pIZM2boNQSXy6Vfc/jwYZkxY4buZ1Ufv27dOhEpfE5dnejTrVs3mT59uvTu3VuAUwfFGLe5YsWKEhMTI/fff79MmzZNateuLTabTdavX3/afWt0Totb+R8ej0dmz55teu3SpUsFgF61Vrp16yY2m03++OMP/dx9990ndrtdPv30U1m0aJEAkCeffNL0PrVjBg8erJ/Ly8uT9u3bi9vt1quOIgUD3blzZ3G73bJx40b93LZt2yQkJEQfcSIi+rvPdLaWUrNmTYmOjpa9e/fq59atWyd2u910FtuZFqyMivNa4+DV+HC73ToO6kw4YxwGDBgggYGBcvz4cR2Hq666SgDIpEmTdBxWrFihk6BJkyaJiMicOXPEbrfrhlHF4dlnnxXAN5Go4hAUFKTjZYyDGhx88803+rlt27bp1Xy1zWqi+nzi8O677+rOdcuWLaZ9C8C0mHq6xa2pU6fq59atW2d6rkaNGoWeTaAe/fr107/JWHfq1Kmj4yAiepLLuL/VhEzJkiX1QOHJJ5/Ucfjss89MZwWqMwA+//xzad++ve4omzdvrn9PSEiI9OjRQ/r27asnvR944AGx2Wzy559/6o49PT1dREQ6deqkj1or6mh34+P222/Xsfj+++8FgGkSNjQ0VOLj4+Xff/+VZ555Ric2KlnMv7ilBgYqQVCTrCoZnDp1qmkRxvhQR90BvokkEZGff/5ZP1evXj3ZunWrdOjQQSeEXbp0EY/HU+hRlsbH888/b6oTjzzyiP5bWlqaPlKoWbNmpqOmsrKy5N5779VxUe1NaGiojB8/XsdBxdXtduvy07FjR71geqY42O123dapfen1enVdV0dYqzr21Vdf6fcWtrgVERGhk22Xy6Xr/NNPP23aniFDhhTZfn355Zc6FsZtAnyL2Dk5OfLGG2/opOG+++7TZyDZbDbZu3ev7Nq1yzQQU22s8WjfTZs26TMw8j9ULG6++WYR8SVfahAYFBSkJzCvv/56/XpjP6XavunTp+uy6HA4TMmDil2VKlUkNjZW9uzZI3l5eXoyVp0BZDwTLv8jKChIqlWrJseOHZNt27aZjg4cNWqULjeqPV25cqWpnqi+VJXLoKAg3T4WVV9cLpdOjmvVqmUafA4ZMkQcDoeMGTNG96XqSCgVD6/XK40bN5bk5GTTZGpwcLCI+PpqFSObzSaBgYEi4jvLU02sBAUFyd69e3U/tWbNGt2X5l/Qmz9/vpw4cUJq1qyp3//GG2/oOhkVFaUnJBctWqTLLOA7w1lEJCgoSMLDw02TYsZ2OjAwUNq2bStjxoyRb7/9Vnr27ClhYWFy9OhR0yC+WrVqcvLkSb2NtWrVEpvNJm3bttXbGB0dLfXq1ZPExES9jcbvCggIkMcee0wA6IVPFZesrCxJTEzURyKqg1kKOyvA+Ljxxhvl6quvFpfLJc2bN9e5gNovaqBdt25dXdbVNsXHx+typNozu90ubrdbDhw4ICIi7du319+l+nl1VocxJzNuU1pampw8eVJvg1psfeONN2Tjxo0SEhIibrdbevToISKnDrBo0aKFbh/VNqanpxc4ilkdrBEQECBt27aVhx9+WE8UqbPTRE7lLbfddluB2KvypI6iHjBggN4G1YbFx8dLjx495OTJk9KgQQOx2+36yONPP/1U/97ff/9devToIQEBAfLmm2+a2ogzPYy5l81mk/Lly0t0dLRup2666SYBIM8884z+DUePHjWV9fwPlaurtqN169Z64kjl6kFBQXoQq8pFp06d9GLK6XL1sLAw3aYOHjxYtzfqYDe1HT/88IMuG7fccosAvpysc+fOpgkYkVO5unpuwYIF+rtVHmHM0zIzMyUlJUX/2zhJZtxXagI5Ojpat6FqIkQdWPH6668L4DuKd9euXfpMplatWsmKFSt0+6IWJV588cXTHrmsHps2bdIT+ioHUpP6anJULVA3bdpUEhMTdfuiypiaaAoLC5OoqCj5+eefTbmIGuSr8qbqxksvvSQioheHbDab/P333/Lrr7/qfqRbt266/1Jntqj/5n/YbLYix3+Ab0JLtYGqL/vjjz/0WMmY59111116X6i85M4779RtjVpMExF9pK56qL7a+FxcXJy4XC49SaQOiPjqq69ME/qvvPKKLlPqYbfb9aSK2jeAr88/cOCA1KxZU9xuty6vI0eOlHXr1uk847PPPhORU+MOFb+KFSvqOhwUFKQXoI1n7LjdbvF6vbJnzx69T9VEvd1u131Wp06d9MShmuwoqn1RZzmoMn7dddeZ+tWAgACpX7++NG7cWGrWrKnbhTvvvFMf8KjanMIeaswRGBioJ+v79eun/96zZ09dptT4U+3zwMBAGT58uF64Uu+ZPHmyqVypPLR///76uZ07d4rNZhOHw6H7HJVjOp1OPf5T9QXw5TSqbVHb/cYbb4iIyIIFC3S5NuZ/ahJ+zJgx+rmjR48WGH8ar66iclRjm6DqnzqbdMSIEeLxeOTAgQOSl5cnDRs21GcqXHvttTJ48GCx2Wx6YknEd8UH42R4Wlqanoxu2bKlPlN54cKF+mj7wMBAPQYorA6rsqkm8VRs1NkuZ2rTAF9ubKxHHTt21NusyqU6227Pnj26r3K5XHLNNddIWFiYHDx4UADfuKhq1aoi4pvDUQuhaoJ869at+kAOxfjdo0eP1uVAHZSnrnaj2paoqCgdu3r16un4qLxTfZaawF+0aJE8//zzAvgmxdUE+NGjR3Ve9uKLLwoA3b/ExcXJsWPHCpQDr9ert1uNvVV+pQ6QWbFihTidTp1fGedwFGMdVrnoiRMnTAe6qtxYxTkmJkbH2ZgvrF27VkREt9P5r9LUt29f06R9/ofH45EnnnhCAOgxpnEh5pFHHpE777xTHA6HaaGvZMmS0qNHDz0HBUDPizidTt32FXVVGuNDnQGo/q0m0UVE551Vq1bVubzKOxs3bqx/p8o7AwMD9XjRGDvjwVvGOQ7jhLNxvkm1I0Wdhawexjk8lXeqxZDRo0frvFNE9AGXqo7++eefppxcHTCjDqTNysrSC7DqNQsWLJCjR4+aFrLHjh0rIqIX88LCwnTeaTwb7YMPPhARXxuqXgdAvvjiC10P1byHms9Sk99qPDxjxgwROTWHp/qcnTt36vk2APrAduP8QmFXhzDOLb722mum8hcQEKAPHF+zZo1uAzwej4j4xqiqHlauXNn02W6329QXqH0/fvx4veiv+shJkybpfFDN96jca9GiRbpPTE1NNY1fVD202Wx6jBUUFCQ9evQwtQGqHqo2VFHl0+Fw6EWSTp066UU+1b/l5uaaypEyefJkXY6MVBsaEBAgW7Zs0WM81UY0atRIvv32WxExz6WqcW7+/Wdsb1Te/sEHHwjgG4P8+eefEhwcLJ07dzbNG+X/nYWN/9S8kdp/IqLz9sIWf4zU56oxqqL60nPdf/mdbvzXunVr/bqhQ4cK4MtVjb9F1bNzWdxSsSvs7DZVn3JycnS/puzfv19iYmJ0Wygi+mD6wr43/5z62rVrBYDpCh0ip3Iq41lhamz66aefmn63x+ORYcOGFfiuovgu9HuWpk+fjuXLl2P58uWYO3cumjVrhltuuQVLlizRr3n33XfhcDhwxx13mN47bNgwiAjee+89/dyYMWNQtWpVZGVl4bbbbkNGRkaB9ymDBg3S/2+z2TBo0CCcPHkSH374YaGvz83NxbJly9C5c2ekpKTo52NjY3H99dfj888/x6FDh856H2zfvh1r165Fnz59ULJkSf189erV0bJlS7z77rtn/ZnFpe6Fo+KgriPboEEDHQd1zwMVhxtuuAGNGzfG0aNH8csvv+g47Nu3D06nEwMGDNBxuOWWW7Bnzx79mQCwaNEiVK5cGWFhYQB81xbfs2ePvhljuXLlThuHnJwc/P3330hJSUFiYiL27NmDPXv2wOVyIT09Hbm5ufjss88AnLoecXFudF1UHCpXrgzAd+3PuLg403vKlStX7PiUKlVK/3/16tURGhqKP//8EwAQHByM48ePA/DFQt0wc/z48XA6nXjhhRdM5Rzw3TdlwIABOg4A9DXDbTYbBgwYAAD6vgsigkOHDqFWrVq44447dBwqVaqkvxuAvrnmypUrMWjQIOTm5gIAtmzZYtr+N954A4sXL0Zubi7atm2Lt99+G3Xr1kVISIi+aeWqVatw6NAhlChRQt+bBPDdn0DV+/wPwHfdWxWL/NdEjo6OxqFDh9C7d28EBwfr+Pz9999ITU0tdN/36NEDAPTNPNV7RMQUBwCoVKkSHA4Hli1bhueeew5RUVEAgKCgIHz33Xd6+5WvvvoKcXFxeOutt5CXl4chQ4bg4YcfxokTJ7B79259DfRrr70WADB79mx9H40BAwZgyZIlOha//PKLfv3111+Pli1bAvBdg1pEYLfb9X0Ixo4di6ioKHz88ce49dZb4XA40KNHDyxevFjHQcX15MmTetvDw8ORl5eHvLw8uFwu037v1asXli9fjvfffx8AkJKSYmrrAKB+/fr6OukigmbNmum/XXXVVahXr16R99i6+uqr8eOPPwLw3TS2e/fu+nPUbwWAkJCQAu/Nzs7G3r17Ua5cOZQoUUL/HmM8MzMz4XA40LhxYwDAn3/+id69e+PEiRM4fvw4bDYbSpYsiQULFiAvLw+A7zrkqg4b7xNz+PBhU7149tlndT+lyuRff/0FwFcXExISAPjK7rfffgsAeOedd+DxeCAiWLRokW6rnnjiCQC+m2XOmDEDQNE3AN28eTM6dOgAEcHevXvRqlUrAEC1atVw8OBBfW+X6OhoAEC/fv3gdrsRERGBI0eOoEGDBvB6vYiNjUVycjIAoGzZsnjnnXf0d1StWhWAr84DQJkyZQD47uOVlZWFKVOmIDg4GEeOHCnQPgLQvx3wxfW6664D4IvjwYMHERoaCgD4+eefkZeXh549e2LZsmVo27atLivR0dFwOp0oX748vvjiC2zatEn/VuDU/Zpshmu7iwhycnLw8ccfo2PHjno7rr32WuTl5el+yuVymfpS475W19gfMGAADh8+DIfDgXfffVfXyZ49e+Lzzz8HABw6dEj3U2XKlNH7E/DdF+nff/81bRvg6/dSU1Px/vvvY8yYMUhLS8OqVatw8OBBfPTRR2jcuLEui82aNdP3IXE6nejZsydEBDfffLPexl27diEpKQmbN2/GwoULUblyZfzxxx/6e9u2bYsuXbqY4hgbG1vo/UuGDBmCgIAAHS8AOl9q27YtAF/ZfvXVV7F//37YbDZ89tlneOedd+BwOPS9NgYNGoQSJUogJSWlQE42duxYnZONGjUKANCmTRucPHkSixcvBuC7t5Cirh+u4lxULtC/f3/Y7XZ89dVXcDqdqFixIpxOJ959912kpKTg+uuvR3Z2NpYuXYq//vpL97/ffvstGjZsaIpdv379TGVi+/bt+h4IcXFx+OCDD/DAAw+gf//+AHz36yiO2NhYeDwe3d536tQJAPD999+jVKlSuOGGG3DDDTfgzTffxPHjx3HHHXcgLy8Px44dQ8OGDU316rbbbsPbb7+Nd999Fx06dNDPq+1W2/bEE09g8eLFet8C0LnAokWLUKVKFfTv3x+7du3CihUrsGfPHn0z+c2bN+s26siRI6YbZE+YMAE333wzAF+ZVrn60aNHkZSUhK+++goxMTE6R9yzZw9EBHFxccjJydHlwpgLpKSkFJmrBwcH6/sM5s/Ve/furf/99ddf6/oTFxeHevXq4Z133sGyZcsQHR2t+2+VI6q6AQC///67/v/KlSvrXODgwYM6J1X/zs7Oxv79+/U+N+aUKk7Hjh3DkiVLEBYWputGWloaTpw4gX/++QfBwcH4+OOPsWDBAuTk5CAvLw9Dhw7VfT7gK4uhoaGm9lmx2+1Yvny56QbS77zzDhYuXIi0tDR8/fXXcDgcmDJlCiIjI/HKK6+gfPnyyMvLg4hg9+7dpnJTpUoVZGVl6XtYHjx4ELt370blypVx//3363uPjBgxAnv27MHhw4cBQJdntR3qt0ZERKBMmTIoW7Ys7HY7wsLC8OGHH6JDhw7Yt2+fzrOM+ZZq71JTU+FwONC3b98C4z+bzYaoqChUqlRJ54XXXHON/v1qrKTi4/F48OKLLwLw3RNI5SXGXN2Y1xj7FJfLhbJly+p/BwQEwOVyIS8vD9nZ2WjXrh1Wr16Nq666CoAvn2rdurV+rTEvUfLy8nRZq1Chgs5vcnNz8fzzz2Pt2rXIyclB+fLlAfjy6+rVqyMyMhIOhwOVKlXCnj17cPDgQQDQ45BKlSqZ7s9pbAON352dnY1///1X1+2XX34Z5cqVQ15enh6zlihRQu8/I2OurnL+2267DUePHtVju4EDBwKAvjdC586d4fF49Bhl//79qFKlCt599138/fffcLlcpjYfONU2lipVCuXKlQPgG+uFh4cjNDQUq1ev1vXkzTffxNVXXw0RQVZWFkQE69atQ0BAAI4ePYr27dujatWqul1LTEwscK8hFWPVRwLAwoULISLIzc0t0OdUqlQJcXFxyM3N1fUlOTlZ58qxsbGoXr06AGDp0qUAYKrXKj8DgI8//hgOh0PHEwAOHDigy3ZISAj27t2Lli1b6nvfqJieOHFCvyc4OBj79u3DRx99hO7du+Oaa67BiRMn8NJLL2Hv3r1o3bo1duzYAcCX577//vto0KABatasqT+jZMmSpm3r378/fv31VwC+8cCtt94Kp9OJFStWYPDgwXp/5i/ncXFxKF++vC7bzZo1w969e7Fnzx689dZbsNvtEBHs3LkTXq8XDRo00PunZ8+eBfqskSNH4rbbbkPDhg0BnMq1AehympeXp3PjzMxMuFwuZGdnIyYmBgcPHsSzzz4LwJdfbNmyBatXr8ayZcuQnJyMmJgYPXYpXbo0UlNTsXXr1kLrgJHNZkPHjh11m3fVVVehVq1a2L17t86NVV3v06ePKe+sWLGirjPAqTJYoUIF3dYGBATo8cvmzZsBQI9DKlasWOA+NC6XyzROUVR+per3J598gpycHHTu3Bl79uwxzeEoKmcFgMGDB8PlcsHtdpv636VLl2LRokX6Hoz79u3D33//bZrDUfvJ6O2339b9uYjg9ddfh8vlQrVq1fRrxo4dq8tizZo19b3mVO4fHR2tP/eWW25B48aNkZubq/NXt9uNW265BW+++SaGDRuGqlWrwm636/LcrVs3hIeHA/CNTVQbVKpUKbz55puw2+26DQagx6pKpUqVdBlWeWdcXBwOHjyI7777TuedX3zxBfbu3Yvdu3fr+hAaGlpo32BsI4zzhytWrND/r9qb/H2Teg7wzRsNGzYMgK9Na968uZ7DU22ryg+GDRum884DBw7oOR/A158lJyfrORzAN6bNyMjQn79v3z6IiCk/vf/++3H48GFkZmbq51Tbqsp/o0aNsGvXLnz99df48MMPdSx2796t8zPjfvj4448L7K/g4GAAwL///gsRwTfffIOAgAB06dIF69evx9q1a9GjRw895zN79mxUr15d30fJmDsr+ctqgwYN9FwRAMTExADwjV8BX1+1bNkyfa/Iq666CsHBwbptNn5e/vs3ZWZmomzZsro/Vvv5/fffx8mTJzF06FA4HA49l9qnTx8EBwdj/fr1CAgI0OOSDz74AF999RUA6D5W9e+qHoqIqU3dtGkTcnJy9BhL1UPVhqr3q3Fsbm6urgMlSpTA8ePHTX2R3W7X5cg49p03b16BMZZR586dERcXh7Fjx+LVV1/VbekXX3yBtLQ01K5d25SrqrE4ADRp0gRhYWGm3+nxeHRO2apVKwwYMEDfC87j8eDRRx8tdN5IyT/+A6DnjYz7T+Xtxb3HVf/+/U35mepLz3f/GRU1l/rYY4/p17z77ruoX7++zlkBICoqCjfccEOxfkdhXn/9ddSoUUPn4Uaq/DscDt2v5eXlYd++fcjJyUGdOnUKjUNxqH2n5sgV1TblHzdVqVJF96eA73dXrFjRNAY5o2Ivg0nR99zKzc2V6tWrS2xsrF7xa926tSQkJBT4jAMHDghQ8NTqNWvW6NXgwlY+s7KyxG63S3Z2tun5jRs3CuC7pq0Cw2ri9u3bCxxpoagjE9Spbmdz5pZaUTZeJ1pRK66HDx8WkYt35lb+e25t3LhRx+H777+Xzp07F7jcFOC7pqiKQ3x8vL5cj8ipOKgjZObPny8ioi89UNRDrUY/+uijhZ659eOPP572/cCpI4CN9xIpV66c3HbbbfL555+fVRyM1wTNH4cmTZroozVETn/mVv44JCYm6vs4Gc/cKux+Aeq03Y4dO5rOJDLGQeRUnXA4HPp76tevb7o8jbrH0JnicMcdd+g6AfguGaB+z2233XbGGKjH+vXrZcOGDfoobHV94KLiAEBf6uaFF14ocN8sdRSZOlJHxcLpdMo111xT6Jlb6kwGdelGVT/HjBmj42C8xIrX6y1w7XvAd7SpyKmjQwp7qFjUrVtXSpQooX93tWrVpH79+iIi+kjtMmXKSGxsrOmMu8Ie6sgrdSk3dQSFOjK0qHucGB/qCIsNGzboI3BULD///PNC2zp1yRpjPTCeDWIsC6qt69Wrl9jt9kLP3DKWm4EDB+p4jxkzRrd1gPnMra5duxYZC7VN6jIgxtO41ecay7o6y6d+/fr68hPqCOLDhw/L+vXrTUflFhZXEdHfZ7zkgPEsvfnz58svv/xSrPphvG55/jO3inN2nbpMk/HoReDUPXIiIiL0Nqpy1rx5c4mMjNT1QMVUHQmmjjydO3euAND33VJ10hhXu91uOop34MCB+mwsdVSdOlNDXX7vdA/jZ6mjcYBTZ26JnOqn1BGdAQEBUqtWrSLPQPrkk090+UpPTzedEabaZVUnIyIipHXr1mdsH9Xl90QKP3NLHX17to9y5cpJ6dKlJSYmRv9OdXlXtY2qfSzqvgbqcccdd0iJEiX02bjqzC3VXhgv8XU2j/z33FIPdQmI4cOH623/9NNPdS6gLt8xatQoqVu3rt6HxiPQFGO7oXIy43d99NFHup0KCwuTPn36SEJCgrRu3VqOHj1quuxR/oc6k8q4jUYqF0hKSpJmzZrJwYMHZdmyZaZyo450PN2ZW9WrVzed+WM8m0Id3fvTTz8JAHn55Zf1UWlqG/Nfj1xdW379+vWmI9BP97Db7fr7z1Smi/vo1q2bztXVJYaL877hw4fLhg0b9FG1qh9yu90FcnV15pbK1Y251SeffKLjDpw6Gr5bt25y44036jb1dPeDUvVDtYFPP/20eL3eQvPc2NjYAuXd5XJJx44d5cUXXzRd0k2dxVjUo2PHjlK/fn2de2/cuLFArl6zZk2pU6eO6Qhvp9Npal/UPlT7QB2Rri6DWdh3p6Sk6DO3ypQpo+ul6mfUJa9Hjx4tmzdvNp11W9ijXLlyIiL6DGd13xpVL4vzUNsaHR2tc2CHwyEDBgyQzz//XFq1aiV2u1169uwpCxcuFMB31L66x1C3bt1EREz5gypTxn3aq1cv8Xg8+oh0lQsa66V6nwL4jr612Wx6X6hL1+U/A1g9ypQpc9ocXUT0GUU1atQw5RsqJ1FjJeMlwQp7dOnSRX+mGiupNvBMR/Yb97+ILy9U/WliYqLcdttt0rZtWwkICJDOnTubLrunHuoMwX/++cd0f9lRo0ZJRkaGZGRk6D4rPT1dPB7PGctUtWrV9GVdbTab3HjjjZKYmCilS5eW+Pj4096TSz2WLFmiy7axrBrHf6rPMd63un79+vqsc9XnGHPM/GW7ZcuWpvGfuipFo0aNRMR89o+6z4PKC0uWLGm6j68649N4P5b8j8OHD5vusbZo0SLT1RJO91i0aJG43e4C9zMXEdNlST/66COdX6h+MSEhQRo1aqQvs1jU49lnny1WHxMUFFSs16k5HIfDIaGhoXp7T3dfO+NDnQFx6623mn6j0+mUFi1ayO+//64fqizPnDmzQOyMZ24BBe8Vra7EoHJj1Yfkb5fU61Q8jGdiKkePHi3yXpDqUoMip3IMdYlORZ0xUr16dWnWrJk+cys0NLTIORxFnfmryoGi6rAq28WJ3cqVK3W7pI6gDw0NlY4dO5ouy1fU45VXXtHjFdWeqkscqjZa7b+3335b1yljPpW/DRg1apTebzExMXpcqT4vISHBdFbx9ddfLyJyxm0FfG3OmfLO5ORkU+xU2VJUPh4fH28aX6hxlGoDjL9d9Z9r1qzR+/WGG24wzaUuW7ZMAN/8QUJCguTm5uorhuRva9R9fozb6HK55M8//9R5Z2HlqLCHakPV7UzUGYzqMmSF3TvM+DDmZ4WdubVz584zbkO/fv1ERExnmZUrV07Xk/zzcmo7jfL3peqscGM7oPKx/PUw/5lbSUlJhfalaiy3ceNGady4sYSFhRU67wEUHOsX9fB6vbocqfujqjkoESnW/jPOG6k2NDAwUPelxvomcqp/U2MsI9WGjho1qsDfevbsKS6XS1/txrg/z9TehISEmPrSXbt2me6JaHytmsMzlvH84z/FOEYV8dVD4/4rivpcYxuqqPxMOZv9l19x77nl8XikV69eBZ5XcxTncuaW1+uVG2644YzfPXv2bNO9SfO3hSJnd+aWutJNYfcpK1GihB4PqG029plKRkaGNG3a9IzbrpzTmVv52e12NGvWDNu3bzcdVXk2PvjgAwC+o13O9TP+C7Kzs/VRXfkZ49C0aVOsW7cO5cuXR2RkJJYvX44JEyYAgD5itjAqDidPnjQ9n5eXh9TUVLRs2RJ2ux3vv/++acX5TKvi6jtTU1MLrFbfeuutAIBatWoBAOLj4wEADz74INLT0/H6668jPT1dn+F0uYkIsrOz8dtvv+kjeDIzM3HbbbcBAObMmYOIiAhERUVh9+7d+Pbbb/WRHuHh4UXGITc3F1u2bMHGjRuxevVqffYWAB1zFYfly5ejZcuWerV94sSJWL58ud6GwkRFRemjFAHfWSROpxNLliwpEAvAd1T0U089pbc7Nzf3ksZBHZWR/+gM+f8j1dR/AeDXX3/F8ePH0b17dyxcuFCfzQj4yvKBAwfQs2dP/dzkyZMLrRO9e/fGgQMH9L/Xr19foGxXqVIF27dvx/Hjx5Gamoo6deroI71UHJYvX246sspItW/qiLj8cTDWsUaNGgHwxUIdlWq323WduBSMR6kZY2Hc/4D5iLklS5boWCxbtgzLly9HRESEqcyrz1VHieT/XHUGUEREhK4T6ohQ5cCBA8jIyNBHSz7//PNo06aN/rvx+2w2G2w2G06cOKFjEBwcrI9cBoC5c+cC8B1tCfjOJjK2VX369AFwqn08XVt644036vfdd999AIDmzZtj+fLl+khDdXaUOqth+/btAIC9e/eedT+ojmhURyAfP35cHz15JkWdgQb44mGz2dC0aVMAviMhe/XqBcB3Bm9YWFiBsqDkP2sW8B1BDADp6en4/vvv9dFPd9xxR7H7qaIY20f13Q8++CCuv/56OJ1O05FR8v9nxaojztVzgO8oteXLl+N///sfQkNDUapUKV0mxowZo7cRAIYOHYr09HTs3r0bO3fuxP/+9z8ARe9TtY3t27fXzxnLSlFtuDo6dtGiRfo9ijoro1+/fpgwYQKioqL0EZjTpk1DjRo1CpwFtnjx4gJ1UgkICNC5gPGo8969e+OTTz7Bli1bdNxOV3bOxFhuBg8erM9GjY+PR+3atfHggw8C8NVddcaocRsL07BhQ3zyySc4dOgQUlJSsHfvXn0G2Jo1a4q1XUV9tlKlShWkpaVh7ty5uh0HUOg2Pv7449ixYwcyMjKwbt061KxZU7d9ffv2BeA7c8t4Nq7xSG9VXh5//HEAvqNtly9fjoSEBCQnJ+PFF1/E8uXL8fLLL8PlciElJUWfUTtt2jTceeedAKDPIm7WrBlyc3P12d2xsbGIiorS3+/1etGyZUvdjgC+/ufRRx8FAH0G7MmTJ3V8AN9Z4gcPHiz0DF6jtLQ0fPjhh7oNWbx4semMCxHRZT1/XtK0aVNT/Rg2bBhyc3NRt25dvPPOO1i+fLk+qrxdu3ZYuHChzmECAgJQqVIlbN26FTfffDPGjBlj2sfR0dH67NwHH3wQt99+uz4Dsl+/fli9erXOUYsrJyfH1L6ofE2d/aGOZMzOztZnWAFAVlZWkZ+p6qWqt6o9rVSpEuLj43WdCgkJQd++fVGjRg24XC7069cPAAocRZ6fKps33ngjqlWrhoiICNPfe/bsqdvRWrVq6aOyc3NzsXjxYqSnp+PHH39EXl4eXnvtNV0n7r//ft1/btiwodDvzj/uOFfVqlXT7bv6vrCwMHz66acAfEfMG4+A3bdvnz5q3MjYNqoymZCQgO+//x6Ar3/LfzSp2v9vvvkmli9frsuUOvq8Y8eOxfoNJUuWRO3atbF8+XLdfy1cuBAej0eXqcqVK+tY1KlTB6+//jree+89HDt2DOvWrcO4cePw1ltvmfpVtX2FtXH5j4Y3/iaXy6XrrKL+Xb16dV1ujPtdfaaxLTG2D40bN0ZISAhatmyJRo0a6bIN+M6gKWz8Z7PZ8Omnn5rGSsYzFi6UsLAwnQ/OnTsXbrcbQUFBpj5r0qRJAIC6deuacl11doOyevVq079VuRo+fDiWL19uamtefvnlM25bdnY2du3addrX5OXl4euvv9ZnxzVv3lyfzaC4XK4CfdZzzz2H5cuXo06dOrqtV1fXyMvLQ5UqVQD46n6DBg30Wfxq3K7mcJxOJw4dOoS1a9cCMJ/FZcx3VP909dVX44033tBXUIiOjsavv/6qz+TKycnBihUrUL58ef2YPXs2gIJHfl9IxvFPUQYPHozp06cDOHU2ozo6vrD86tixY4XmTV26dMEnn3yixwWHDh1C586dC9Thc82NVYzKlCljalvUthjLdtOmTfHll19i0KBB2Lp1a4GzKAuzb98+1K9fHwB0DhIaGoqcnBycPHnSNHegvstms5nyKWMbcCGo8cCCBQsA+M7KadmyJZYvX45GjRph8ODBWLZsGQBfv3H33Xfrs8rUFVOKo3bt2jo3Vu3S2TLO4RmvugMAjzzyCB5//HFERESgWrVqWLhwof5b/jNqAV8b8f333+u8U80nlS9fXrfzERERKFu2rP79xaHiNmjQIFPOCvjajsLGL+vXr4fD4UBycrLen9HR0Vi+fLnOz4YNG6brujpDW51BM3v2bKSnp+v+W8XyfGRnZ+PQoUOF9nl//vmnnjcCfOX6dH0p4JvXOXjwoJ73KFu2LCpUqKDrcq9evfDDDz/ofAnw7f9XX33VlHuXL19elyP5/zOzjGM9tf+MbajxCl4TJ040zRv9+uuviIiIQIkSJXRfumDBAl3fgFP9W/6+4ExcLhfsdjvmzZuHrKws0xm0atwC+HKe/PO+ISEhpv1300036TPcOnfuXOS8kVLUGM04RlX1sLhnbRWXsb0Czn3/XWxqjHc25s6diz59+qBs2bJ44YUX9Fx/8+bNz6nfMSoqv8yvqDmFouaZCnNBFrcA6IkTNUBLTEzEtm3bTKftAacuNacGZADwww8/YNy4cbjppptQq1Yt3HLLLaZT/5W8vLwCA4nffvsNAEwLAUZRUVEIDAzUpzjn3xbjpamKu+ON21/U50ZGRhY4tfVCWLx4sWkyJT8Vh4MHD2L27Nlo0qQJ9u/fj3r16pkSbhUHr9eLbdu24ciRI6Y4qH2iOs2yZcti3759KF26NPLy8pCcnIzMzEz9UN+blJRU6H6sVKmS3i7j+zIzM/Wki7rsgnp/8+bN8dJLL+Gff/5B+/bt8fDDDxc4lf90cQB8p77mj8O2bduKLC/FtXjxYhw7dkxvz5133om6desC8A10Dhw4oBvqkSNH6on6Q4cOFWiUjZcYePnllzFv3jw4nU4sW7ZMXxbyxRdfxMGDB3UcWrRogdKlS+vKHhoaiszMTFSsWFHXCQCmjhQ4NQnqcDiwadMmdOjQAddcc02BWKj4q21NTU1FQEBAkXEATg1aC4uFOl1ZLUIo0dHRpstznavc3FzUqFEDkyZNQrdu3fQl2wBfErNy5UpTx9u4cWNkZmYWGIT27NkTNptNtz/qsoHAqbZGlafSpUtj3759qFWrlo61ikNmZqbuWI4cOaIHSKqO1atXD4CvrP/555+mOBjrmPHSIyrZdblcuk4Ap+q8upSTSsiMduzYYRqk5V8g/+2330ynYhvl30dGqg0tXbo0Pv30Uz3gbtq0qY5Fy5YtkZ6eXuh2nY66hIbNZsO8efPgcrn0KdobN25EZGQk1qxZg7179+pJ4hYtWhS6oAL4yoiqL8Z+SsUpODgYr776KmJiYvQluJo0aaLjGR0djVdffVX3U06nEzk5OQXa47y8PAQFBSE3N1e/V00SVK9eHZmZmTrhVTIyMhAYGGhavNu7d6/+/QDwzz//IDExscAEqfq9TqdTL3yqbVy3bh2AgnUyLy+vQBlQ5Sv/5HR4eLi+lITT6UT16tX1pc7atWuHw4cP4+DBg7p8bd26Vb9XtYlGqm3JP9Hi9XoL1EnVlzocjkLLj6qTBw4cQFJSkql9VJMLCQkJWL16NTp06GA6xV9N8KvLUxl/u4od4GuzFyxYoC/zUrFiRdM2NmrUCC+99BIaNGgAm82Gt956q9BtVGUgKSkJ+/btM8U6f1k5cOAAsrOzTX2pulyvx+OBzWYzTYZnZGQA8E1w3n333QgMDNRtd506dfDXX3+hQoUKsNvtenK7Ro0aOHDggC5Hxpzs999/17mAakt/+ukn9OzZEw6HA/Pnz9e5WFH1rbA+9vfff9c5WXZ2NrKzs7F9+3YkJSVh8eLFehsHDx6MdevW6UWpqKio07ZDxu2PiIjQ26jaDVXfitMGHTt2rNB/BwcH4/fffzcdCPHRRx/pS5TGxcUV2MZZs2Zh1apV6NChA/bu3YvZs2ejSpUqekFd7aO0tDQdf8A3Qaj6FFWmVV/eoUMHZGZmIjU1FSdPnkSfPn2QmZmJffv2ITs7Gx9//DGuv/56AEC9evUK5Buqv3A4HAgJCUFgYCD27duHevXqITMzE06nE6VLl9b7Xu1XNYn5559/IisrC6GhoZg3bx527twJwHdQj9oPheXq6rKQx48fR4sWLTB58mQAvjxvx44diI6ORmBgINxut97nxrwE8E3MVKxYUdcLdTmrihUrol27dsjMzNQHBYwcORLdunXTlxY+fvw4ateujW+++Qbz5s3Dtm3bAPjqZcWKFbF37169MFa9enWMGTMGDocDW7ZswXfffQeXy4V27doBKNienjx5Eps2bTLVIUW1L4cPHzaVv8zMTIwYMUL/++mnn0ZMTAw8Ho8pJzLGb+vWrbpeqjKtcipVltTC6AMPPIDnn38eNWvWhNvt1n9Xl01RZVXlcqpexsbGwm6349ChQ/jpp590G672+YIFC/Q+vvfee7F582b9uxcuXIj27dvrgzReeeUVzJo1C4Bv0veRRx4B4Ou/jx07Zrqkc3Jysj7oQ5X93377zTSmKOpgjfwT0KqfFBE4nU5s2rQJx48f15M+Ko5Ko0aNcPXVVxf62YrT6YTNZsMvv/yiB99ut1t/t9q/qh+++uqrkZmZqS+VpPo948ECNpvN1AYqXq8XSUlJWLduHapWrar7r5ycHJw4ccK0rapePvHEE/jnn3/0YuSsWbMwZMgQvR1FtZ8qZ/v9998RHh6OAwcO6D5r3759SEpKgsPh0DmpkZqEDA8PN/VVqo0JDg7Gtm3bdN+VkpKCQ4cO6UsiVq1aFUePHkXp0qWxY8cOjBs3Th+09d577+HJJ580jf9UDpe/fVfjy/x9jsrjoqKi9EFUf/zxh+l16vdXqFBBxwTwLQB89NFH2L59O1599VW0b9++QDlTl4SuWbOmKdc9dOiQqZ/+6aefCt1vLpcLmZmZprbmr7/+Mk3uJCYmFhgj5Z8H+P3333Xc//rrLxw+fBjbt29HdnY2nnrqKRw7dgwJCQlo0aKF6XPq16+P8PBwUxuTmpqKzMxM1K5dWy9SqrJbtmxZPZa7+eabsWrVKn0ZyS1btpjmcFQ9WLBgAdauXas/K39urLbpxIkTOHDggKn/DQoK0p/l8XhQu3ZtPZm6aNEifYDnRx99hGPHjhU5h+N0OvVl4hTVb6m2XN1yATiVdwI44yUPAV88unbtCuDUXEeNGjUAoNDbXuTl5RW6INGhQwc4HA7Ttj777LOnrcPGcZvxgDjjPECFChVQtmxZ3a5ee+21prYl/0GDaj/Wr18fDz/8ML755hu88sorAHw5snEBY8SIEVi0aBGqV6+Ot956S+8v1Wenp6fDZrPBbrfj4Ycf1gcwqHKjtr93795YsWIFxo0bB5vNpuuAse6odkX+/9LJqpwbb4NgHDOr12ZmZqJr164IDAyEiKB06dJ6TLd48WKdW33++eeYMGGCPhChOJO56nLv4eHhBdolwNeWn24uUI05VMzVb1S5QmhoKLZt24YFCxagWbNmGDVqFDZs2ICvv/4agC9W6ruM8yvJyckYMGCAzjtVuYyMjNSxueWWW7Bx40ZdLgAUyPvUJTlTU1MRGBioy0arVq1MOas6AM2YnwG+vvyTTz5BgwYNEBISgqioKDgcDn25eJWfORwOHUeVh6nt7NChA1566SW9qLVkyZICc1BFHQyq9s22bdsQGhqq97O6PUf+y/UDvrbo9ddf1+17ixYtCu1LVbv566+/Yvfu3bDZbBg3bhw6duyIPXv2oEqVKnoOPCkpCampqbrtf+6557B371789NNPej86nU5UqVJFl6OcnBzYbDbTgTdRUVEICQkxtaGZmZl6nBYaGmqqAyKCgwcPokOHDqa+9LrrrivQv51ujFXY/lX5GXDqMteKGrcAvv4j/7yvsd06cuSIvvx5//798eabb6JEiRLnNG9kHKOqeljYgUvF/Z2qjcmfW6jxX3H339lKTEwsdJ8XNr+q8jajkydP6jxcKVu2bIF+ML/FixcjJSUFS5YsQa9evdC6dWtkZmYWqG9nu15ivMy3snPnThw4cKDQcdP5uiCLW9nZ2Vi2bBncbre+xma7du2Qm5uLadOmmV47ZcoU2Gw2fW+I7Oxs9OnTB6VLl8ZTTz2F2bNnY+fOnfponvyMnycimDZtGlwuV4GkTXE4HGjVqhXeeOMN05FDO3fuxKuvvor09HQ9SFQdUHEqU2xsLGrWrImXX37Z9Pr169dj2bJlBQZPF8K6deswdOhQPVDIT8VBTZyIiI7DU089hWeeeUa/VsWhZMmSyMnJwTPPPKPjMHHiRP0damW6e/fu2Lp1q06YjHE4evQonnrqKR2HoKCgAvvQ7XajTJky+Oeff0xH5qg41KtXT8dBTTarz3C73ahSpYo+Y8qoqDioBiAnJ8c02Qr4Bjeq/J2Lffv2YejQoQgPDy90QWD27NnIzc3VDbvx+vC5ubn6CGhlypQp+v+nT5+OuXPn6kmGw4cPIzw8HPv378edd96p4/Dcc8+ZPmPWrFk4dOgQDh8+jGnTpunEMP+9r2644QbYbDbk5ubiyJEjpvvj7Ny5E/PmzdN1Yu/evbpOnDx5Uh9hVVgcAF/9UbHIn9Dv2rULoaGheOWVV3D48GF9HeQyZcoUuEb22VAT9DabTZ9ZAcA08RsQEFDokQAnT5401QnAl/iVLFlSJ1UNGzZEZGQkTp48iZkzZyIyMhLr1q2D2+1Gr169sHXrVjidTp0Ez5o1C9nZ2Th27BgmTpwIm82mFzv27dun69j8+fPhcDggIjh69CiaNGlSYPsmTpyo/3/Pnj2YNm0a7Ha7viauOoJS1Rf1Gzdt2mRq6wDfkaMq+bDZbPq62AcOHMDXX3+Nr776yjTpYhQaGqoXno2v2bVrF5YtW4aAgADUqFEDIqInr/IfYXHPPfec1VEk69at00fFbt26FXPmzEGbNm30QOCzzz5Du3btCj3i0Pg9xrNVVDtgt9t1P9WyZUv93tmzZ+PPP//EtddeqxPV/P1UbGwsHnnkEX3EKHDquvhGZcqUweLFizF9+nSIiD6bp1q1aqbrUqv9abfb0apVK3zxxRc6aUhISMDOnTt1LP/44w+0b99e10lVb9QZVR988IE+i6py5coYNmwYsrOz4fV68fLLL2Pfvn1YtWqV/m41uFT7T22jOrpTqVy5MhwOB06ePInSpUvjjTfe0PXu3XffRW5uLjZt2oT09HQkJyeb7h1ivI644vF44HA49D2xQkJCYLfb8fzzz2PXrl26Tm7atMnUl+aPq6qTarKkbdu2pvZR5QQvvfSSjqsaxK1btw7Z2dlwuVy4/fbb9eeqdu3999/XRxuq7Vf3pEpJSSnQbgDQk6nG36q2MSoqCrGxsQB8k7xbt241TeIsXbpUP6eOxN++fTsCAgIK9KUrVqyA1+vFG2+8YYpR2bJl8cQTT2DTpk0QEWzfvl3nZHa7HfHx8cjNzdW/8YUXXkBubi42bNhgKuuA7z4KKidT7dDSpUtx4sQJtG3bFnPnztW5gHFwqH57UTnZrFmzkJeXh1atWuHo0aP44YcfkJOTg7Zt2+qF/vT0dNx0002w2Wz6rAe1cH86YWFhKF++PBYuXIjMzEzMnTtXXwddbavqI9Sg1DioV7Zv326KodrPNWvWxI4dO/QAWx3d+9JLLwGAPjvTKD09Ha+99pq+Vrkqw2riTy3w5JeXl4eZM2cCOJV7Pfroo4iKitKT5ep5lYsZy6qaQDp8+LDevkOHDukcEfBNPHft2hV///23KVdX+dtjjz2my8XevXtNB1FMnTpVLx4PHz4cH330EcaPH4/k5GQ9eM+fq6vJse+++86Uk6l8NS0tDa1atcLu3btNB8WpHBHwtZNHjhwxTRYZY3Xw4MECZ/94vV59IJD6m/H+NY0aNUL37t2Rm5urtxHw5QJt27bFnDlzdP/TpUsXuN1uPP3006bvfeGFF3Dw4EHT2ZiAr01Q7cuMGTNMk2XDhg0zXVd+1qxZ2LlzJxo1aqSP+rbZbEhKSsLBgweRl5eH3NxcBAcH46mnntLl4M033zSVC7XYbDwDRET0EdJqcVvtw7179+Kff/7RY6Vdu3YhLy8Pb7/9Nux2u17QUHGtX78+pk6dCsA3mWaz2XS+V7FiRb1IAPgmvPv164eaNWti9erVWLNmjT57evr06fjggw90Dr1lyxZ9D7qXX35Z5yVt27bVZcQ4ODbu//z5nZq4Bnz32BURuN1u/Z7du3fjzTffNL2nOAcjigj++OMPPXn0zz//ICgoSO//9evXY8+ePcjLyyuQoyszZszQbXBQUBDWrVun20Clfv36ehJiyJAhul0cOnQoSpQoocuUOgBGUWcXATDl6IXluoqq10uXLkV0dDR+/vlnTJ8+HeHh4diwYQPq1KmDAwcOIDc3t8h7Lqg8Iz91v6sZM2bA7Xbjr7/+wvr16/WZe7t370Zubi6OHj2qc2N15su+fftw5513msZ/qv4kJCTo9r1169aYM2dOoX3OL7/8gq1bt8LhcOgJ002bNukF5507d+KHH34AcOqMG7X/1L2C1P4v7Ohvl8uFkiVLmsafU6dORW5uLk6cOAERwYABAwrkvtHR0WjatClmzpyJ7du367ZG/abatWvr17Zu3RpffvmlPgNq3bp1GDJkiGlyadasWbpvmzt3rqmtMd4LL/8EpJqYNpZ9tZDVsmVLPXZRf+/WrVuB3FhN6Nntdp0b79y5E7m5uYiKisLChQvx2muv6YXBMmXK4PXXX8f69eshIpgxYwbsdju+/fZbzJo1Sx/trxayVZut7vsB+Ca7GzdujK+++go1atTA4cOH8eabbxZZh0UEb775pr7v8Ndff43vv/8eUVFROnZqYX3u3LmmvPOnn34qcCBWfmosZ6Ryjl9//VXn+cacWx2AYlSyZEm0bdtWn70GAPPnz9f7QdXhvLw8TJo0CVu2bDGVg2nTpiE7OxsnT57UZ5IBvrLdvXt3vfDUunVrU9lWB0cpgYGBRc6DHTlyROey6n7S3bp1w9VXX42PPvpIn8mj5h4aNWpkutqKam/UfXHUmK5bt27Iy8uD0+mEiOjJ5KVLl+rt/vfff/W+DAsLw4wZM5CTk2M6G9l4PzDAd9auyuVV3nn48GE9DrPZbDhy5Ai8Xq/eFnXvrMIOss9P7Yu33367QN4J4IwTt6perFu3Dp9//rmeS1Xju7p16+r5JBHReafKHdW+zc7ONt3X8/bbb9cxFBFERETAZrOZJrdbtmyJcuXKmc5iV2VUlf8vvvgCUVFRuOqqq9CyZUu9T9TBSqqvjY2NhcvlMuVnHo8HH330EXJzc/HAAw8A8NWVtLQ0HD9+HE888YRpDm/KlCkICgpCRkYG1q9frw+4HDt2LIBTC1XGXE758ssvTf2TOmhBjfXfeOMNtGrVCm+99RZeffVV3Yeq+7ka629UVBSGDRumx7nGv23ZskXfhzgiIkLng2r8N3PmTJ0PqnbB4/HoxW51sJA6sMc4pxIYGIjDhw/rviA3NxfR0dGmhXCHw4GuXbvqNjS/6dOn675/7969uo60bdvW1Jd26dLljP2b0dKlS/HHH3/gyy+/BABTfgbAdM9m4NT4pDDHjh0z7dN77rlH32978uTJSEpK0vcNP9uzj/L3pW3atCn0QIKiqDk8xbj/jK677rqz2n9nS90nVi1iA758ad68eQVeW7ZsWX1Wo/F35N93Xbt2xbp16/Rcj5GKR2Fzal999ZWOu6LWCIqzXqLakieffNL0vGrD8o+bLohiXr5QRE5dk3LcuHEyZ84cmTNnjkyaNEnS0tIEgNx77736tbm5udKsWTOx2WzSv39/mT59ur6e6dChQ/XrRo0aJTabzXSdy4ceekgAyDvvvKOfy8rKEq/XK+XLl5fevXvL9OnT9XXU77//ftN2It91INevXy9BQUESFxcnDz/8sEyYMEFSUlLE4/Ho+2KI+K7L7XA4pH79+jJ79myZP3++7Ny5s8j9sXz5cnE6nVKpUiWZOHGijBs3TqKioiQ8PNx0L4JzuefWHXfcIXPmzJHZs2fLpEmTpEuXLuJ0OiUiIkLuueceUxzUPVdSU1MF8N37Jjw8XBITE2XixIlSrlw5AaCvBa/uMzN06FB9zXV13fzBgwfra16reL3zzjuSm5urrxnrcDgkKipK6tatK+np6fq6nCoO7dq109dO7dq1q97HK1euFJvNJjabTRo0aCBdunTR92cICQnR+yA1NVVsNpskJCTITTfdJO3btxe32y0dOnQodhzUfTYqVqwoSUlJMmHCBH2fnuDgYNm2bZt+/+nuuZU/DoGBgWKz2SQiIkJWrlyp71tRv359vd/UPZbw/9fPTUxMlJ49ewr+/7q36rOHDBmi93FcXFyBa/Wre27NmjVL14m33npL2rVrJzabTZKSkvT3uN1usdvtpmtPh4eHS1BQkEyaNEkASN++fUVETPeXCAoKkk6dOkmbNm10zFS8ateuLS1atBC73S7BwcFit9vF6XRKq1atCsQAgNx+++06Fmo71LWsHQ6HjklMTIy+j0tgYKBUq1ZNkpKSCtxzS92vS10z/o477tDlMjw8XMqUKaPvO1G5cmWx2+3SqlUrueaaa8Tr9Qrgu+53VlaW7Nmzx3TfgDvvvFNq1aolNWrUEMB8n70qVaro15UrV0569+6tr62srkN+7733muqEuh5zYGBggeukp6enS7t27cTlconNZpORI0fK6tWrdfsF+K5LPmTIEJk5c6bUrl1b7Ha72O123dapz4+JiRG32y3Dhg3T+9DY1gG+a9Ortk61FU6nU5YsWWL6DYDv+tmq/pYtW1aSkpJMdULFYfny5bpMqbgEBgZKeHi4lC5dWrKysuTZZ581XSs5MzNTunfvru8dFBgYKFlZWTq+TZs2FQDSo0cPXcdULFVbp673DUBuvPFGueaaa/T+/vPPP2XPnj0SHh6uy9bIkSNN9Sg+Pl569+5turdWYGCgDB48WKZOnWp6XpUXY1x69uwpU6dO1fdlCAkJ0fc6VHUSgERFRUnXrl2levXq4nQ6dflzOp2SnJysX9egQQMJDw/XfalqD+rWrSutW7c27b+EhASJiIgQu92un8/KypJHH33UtM1PP/20AKfu+6euse10OnVs1TXqje8zfpe61xdw6h5r6nNGjx5t+k673a7LnvGz7rjjDtP+tNlskpycLK1atZLU1FR9reiuXbua3h8REaHvs+N2uyU2NlaXTdWXZmRkmK71HhMTo+91YrPZJD09XfLy8nSdtNls0qxZM/16h8MhQUFBMnToUN2Xulwu8Xq9MmnSJJk/f76sXr1a70uXyyUul0uaNGkiLpdL12e73W5qNwDfvc+ef/55iY+P1/sdgIwYMUL3CbNmzdJ1aufOnXob1Xu8Xq94PB79PS6XSyIiIqRFixb63mClSpUS4NT9Zoz3duvYsaM0bdpU96/qc4KCgqRkyZK6/TDe1ycsLEyXD5WTqXJpzMmM962Ijo6WG2+80RT7lJQU3U6pdtKYkxlfm5qaKo0bN5b7779fP5eYmCiPPfaY3rYePXrIzJkz9b4BfPdhUjmZKkf57/2qrgFus9lM93FT+6Jly5amnAHw3ZPy+eefl/nz58vjjz+u91lgYKBMnTpVBg0apOvJrbfeKqVLlxaHwyEZGRmmMuB2u+X48eMF9pe6Lvozzzyj93+dOnXE7Xab7icwYMAAmTFjhr6ngtvtlujoaBk8eLA8/fTT+lr2devWlalTp8qTTz4pAwYM0H1Kz549ZeTIkeJwOKRkyZK6rqt+UpUptU+DgoKkffv2smPHDlM/2rhxY9P9Q9u2bSurV6+W2rVr6+cbNmwobdu2FafTKVFRUaY25McffzTl6mofGdtUr9crYWFh+h4xql798ssvOldXMatRo4ZERkbqOuXxeGTNmjU6V1dlvVy5cjJy5EgpW7as3lc33XSTzJw5U6666ir9+1T7brwP29KlS0VE9D0yVTs7bdo0030SFixYYOob1b0Ia9Soodu6xx9/XI+HVNnr3bu3tGzZUmw2m273PB6P5OTkyJ49e6REiRJ6+4x5EuC7N5Uql6pMBwUFyeDBg033MWrevLnMnz9f/vzzT1P9DA8PL5BXqnsnGPv4wMBA6dSpk/Tt29d0nX3jNqu2zGaz6bqqckLVjg8bNky/X+Xx/fv3l8GDB+t6lJ6eLoGBgeJ2u8Xr9Zr6IdVvqs8uUaKEbNu2TXr16qWfU7GtVauW6X0qR1fbqD43MDBQ7Ha7KfdW4w7176CgIBk9enSBez3cddddus1QY6XC7qfRrVs30/hPtcU9e/bUeau672zZsmWlcePGMnXqVJ2LJScny6uvvirTp08XAHLLLbdIRESE/i6VD7rdbtN9LGvXrq3vZfTYY4/JsGHD9HsSExNl0qRJMnnyZFOfpe5btXv3bsnIyND30IuIiDDlyG63WwICAiQiIkKqVatW6D3JVN5Qo0YN0712PB6PBAUFScOGDXW7kf9eLXa7XUJDQ/XYRfU5ap+o+3hec8014vF4pEOHDrptUTkCAH3/M2Ofo9rg6OhoPf5r0aKFfk98fLx06tTJdB+VvLw8ETl1f66YmBi9z0JDQ+X48eMF7mHRu3dvHZtSpUpJnTp1JDQ0VOdbqh021kE1D/DTTz/p3PXee+81tT9qn91xxx3y5JNPSmBgoL7nld1u122een1qamqBe3kmJSXpnCIoKEjq1q2r+wL1uPvuu2XSpEmmvCA4OFjatm1rGksGBQWJ2+2Wtm3b6udq1aolPXv21OXHbrcXyI1V2+D1eiU6Olq8Xq+kpKRIUFCQuFwunRsb20x177ZSpUpJu3btpEePHrptBCCVKlUyzeGsWrVKoqKipEOHDrpfAHzjRJXPVapUSRwOh7hcLmnQoIEEBQVJQECAxMXF6bkDdX9am80mXq9XRo4cqbdp3LhxOnaF3XOrd+/e+nvVWCE4OFi3nWXKlJHq1avrulmyZEnZv3+/fr+aB9i0aZMsXrxYf69xbBYXF6fv+ZiYmCh2u122bNkiGRkZum+w2WxSsmRJU3mrUaOGzo1VW9GqVSvdHrndbv1+NQ6uXLmyzqFvueUWGTx4sG4bYmNjdblW7WGpUqV0rI11DPDd40i1vWXKlNH3XwJ8Y4yqVauKiG8u0Ng2qHio+mds/43PlS1bVt8P0Vi/1L/DwsIkMDBQhgwZovs8h8MhXq9XJkyYYCr71atX1+VDvS4iIsKUdwLmMffXX3+t973KPYzbZ7xvTv57bo0bN07P3xl/m7Ge9uvXz3T/ovz9eXJysmRkZOi5VON+UHl2dHS01KlTp0CMqlSpYroXnsvl0nmn6ksBX+48depUnfOoz1H5GeAbAwcHB8uYMWPkkUceMW1LWlqaPPvss3puadOmTbps165dW88FqG1QfanD4dDtS7Vq1fS9FUuUKCFTp041jTGqVasmkZGRMm7cOJkwYYJ+X8eOHXV5LFGihGluxuv1yrBhw6Rq1aqyf/9+XQ+XLFkiJUuW1PmIx+ORbt26Sf369fV8parrqkypOqH2jWozAd88ZFxcnAwdOtSUn9lsNrnrrrvkySeflIEDB+oxqfF+xh6PxzRGFRHZsWOHJCYm6nI9c+ZM6datmy6zKr9Q+XliYqI899xzet5IzaWqvLxEiRJ6/JKf6ktTU1N1uYyPj5eAgAAJDg6W0aNHS+PGjfXvBHxjVOP8WPny5fW4ZeDAgVKyZEmJjY2VrKwsWbFihdhsNqlRo4Y4HA4ZMmSIvm93SEiIREREmPpcNUbNP/4zMrahKm8/E/W5aoyqxn92u13PL+RXnP2XX1ZWlng8Hr2Gkv+h7s+3bds2iYiIkPDwcBkzZoxMnDhRypcvr+ur8Z5b6h7SXbp0kRkzZsjAgQMlOTlZIiMjTfvu33//lSpVqojD4ZB+/frJs88+K4888ojUr19f1q5dKyIiL774oq43M2fOlHvvvVdKlCghVatWlcTERNNvqVKlipQqVUqmT58u8+fPlx9//FFECp9TV2W6e/fuMn36dP3vzp07m16XmJgo7du3L7Df1P1gi+ucFreMD6/XKzVr1pQZM2YUCP6///4rd955p5QuXVpcLpeUL19eJk6cqF/37bffitPplMGDB5vel5OTI3Xr1pXSpUvrBEDdhG3jxo3SqlUrCQwMlJiYGBk9erTk5uaafxQK3uTsu+++k9atW0twcLAEBgZKs2bNZNWqVQV+43PPPScpKSm6ATROehfmww8/lEaNGklAQICEhoZKhw4dZMOGDabXnMvilrGhj4qKkiZNmsjDDz8su3btKjQOqqNScfjiiy+kfv36EhAQoJNuNWiOj4/XccjIyNC/t1SpUnoxZtq0aQXicPLkSUlLS9ODLKfTqRc+1GS/iO/mesbExFi5PvzwQylTpozu+Nxut9SpU0dmzZqlXzNz5kwpX768qbPv0aOHHDx4sNhxUAOZiRMnyqRJkyQhIUF3pk888YTpvadb3MofB4/HI7Vq1ZJdu3aJiOjBqPHhdrvF5XJJ/fr15fPPP5f69evrTu66666TLl266E5Q1YkmTZpIlSpVTDfnVnEQMdeJXbt2yYQJE0yDRTWAt9ls+gaPq1evliZNmuhBaY0aNURE9A2+k5OTdeetErN77rnHFIcmTZroRE893n777QL7H/AtbqlYqCRIff7w4cNl0qRJpgE7ALnnnnuka9euUqlSpTMubqmHGkzGxMRI8+bNC60L6jfHxcXp8mfs8KKiouTuu++WDz74oEA9b9KkSaETGC6Xq0Bbd/LkSZkwYYIebKv3qcHt9ddfL3v37pXXX3/dNOmclZWl4xAREaEX31Vy43A45OGHH9ZtXWhoqJQpU0aXo7Jly8qIESMKtHWq81BtndoPDzzwgGm/9urVy1S/AN/NtytVqmSqE+r16rNjY2P1Z1aoUEE2bNhgGvDPnDnTlOgBvmT8scce06/Lv7hl3L8qEVNt3YkTJ/Risdfr1eVp+fLl+jd/8cUXeiAeExOjF/nzP5xOp8TExMiaNWukQYMGuq2bNGmSvmG93W6X8uXLy2OPPSaPPfaYVK1aVW9XVFSUjB07VrdDOTk5kpqaqttB43c1b95cqlWrpv+mykVqaqrMmjWryDbc5XJJyZIlxePx6PeFhIRI9+7dpV+/frovjY6OLjDZpJKwhIQEcTqdEhoaqm9cbbfbTZPEDodDbrjhBlN9UclGYYtbIiKvvPKKaeLb+LDZbLqfysjI0GUgf10HfItbamE0KChIrrrqKgkICDBN4AG+SXTVl2ZkZJhuLm587Q033CB79+7V5UHVSeOCnfrNkZGRui9dtWqVqX3MysqS559/XgCYJgyMvzEsLMzUbgC+BNzj8YjX65XIyEi59957BfANSoxtuKpTu3fv1tuoBuXGdsNut0ujRo1k3bp1ui817kflvffeKzIWxjY9KipK1yHj5zidTnE6nZKWlqbbM7WNPXr00N+j6mtCQoKEhobqONhsNgkICDDlZKqvMOZkxm375JNPpH///noSICAgQC8mNW7cWNq3by+RkZESHBysJxY9Ho8EBwfrtlOVo/yDm+zsbHnuueekUaNGpsXT0NBQGT58uJw4cUK/VuVZ8fHxuk707dtXAF8/GR4eLuHh4RIcHCyZmZkF6pmaPFcTPv379y+wv1RfpgwbNkzvN5vNJnfffbc8+OCDAvj6yaCgID1BHBYWJt98842pnerUqZNUrVpVPB6PhIeHS1pamowaNUrGjh2rn1dtfv72yPi9gC9fUAOInTt3Sr9+/UyTPKp+qXrxwAMPFPg8u90uUVFREhoaKmFhYfLPP/+IiDlXN7bLalKratWqpknlwMBAefPNN/V++u6770wL18bFyquuukq3v88995xe8FXfMWHCBJk2bZrud4KDg6VBgwbSvn170z5xOp16MthYjlSM1IRf1apVxev1SmhoqBw7dky/btq0aXqi0dh2R0VF6QF+/ofD4dB5gnFAZ+y/VP1Xcfroo4/k8OHD0qZNG/05pUqVkgYNGpgWwtXfXnrpJcnKypKAgADTQSwA9CTUM888Y6rrsbGxpsX7xMRE02Si+lv+9jD/Qx3UVLZsWfF4PLJz507T+C8uLk6SkpLE6/XqbXY4HLrfadiwoWkBym6361xTLWoYJw/z56Vq/wO+/jc+Pl5/3m233SYtWrQw/U41URYaGipRUVESEBAgYWFhBSYAlYyMDKlataqeRFKP5OTkAuM/1b7nzx8AyLvvvqvbwMDAQFNfribG87flgG9CtHHjxgXGSqocu91uKVu2rG4L1PivdOnSpj4r/+KW8cAtYy5js9mkYsWK8sEHH0hWVpaUKVOmQFtoLNs1a9aUDh06mBa8VSzKli1baJsUFRWl8wQ1D6C2Ty0SORwOGTx4sBw8eFC3LT///LOOU2HzAKoNbtmypR7/qddXq1bNVP5UW2Okvl9t89VXXy0iBW/Qvn//frnpppv0a9V4OCAgQDp16iTXXnutADAtChnnATZu3Ci9e/fWiwM2m02cTqdpYUXVLXUgQ2hoqDzwwAN64Qbw9asq71JjCK/XK2lpabofDQwMNG2Heni9XgkODpbY2FiZO3euafI8PDxcKlSooPvTcuXKSZMmTaRUqVIF4mlsm1VubBwHREdH636hadOmuqypsZzKVbds2aL3uTq4CIBuPwqbw+nTp4+4XC7Zs2ePPPfccwXqzsSJE2XkyJG6PbDZbBIXFye33367vPfee9KoUSNdPmJjYyUkJESPNdq3b2+awylscWv//v061sb2WH2Xiq36m/HAbRHz4taJEydMubFaJHW73br9atOmjXz//fe6XVI5mbFNtNls0qpVK1NurPZNSkqK3pbIyEjp06ePAKfGwUuWLJGoqCi9Dz0ej7hcLqlVq5bs3LmzyHG3WuRV/ZRql9TBsbGxsXo/Dxw4UJo0aSJVq1bVc4GqnwsODtYHpyUkJJjGOqrcBQYGSmxsrCnvNNYv9e/hw4fL7bffrueA1AEbHo9H553t2rXTBy7bbDapXLmyhISEmPoClXcCBecXx48fbzooRZUb4NS8iEjBxa38/ZcxbzUe6GH83cZHx44dJSMjQ2rXri1Op9PUPi9atEhycnKkcuXK4nK5dF5hbIvV71VxDg8PN+Wdqt5FRkaK2+2W1NRUGTNmjLRq1Uq32/nnSmw2m5QoUUISEhIK/E5j27l+/XqpVKmS3ha73S4BAQHicrl0XzpmzBhp3Lixjo36nNTUVPn2229N7fXtt98uc+fOlfLly4vH4zEt/qu2Pjg4WG+3alNTUlLk9ttvlxMnTpjq4bZt2+TOO+8sEKPGjRvrA4Vbt24tIr580FhGVR5XunRpKVmypHTt2lVuvvlmKVu2rC5Tqj0z5vODBw+Whg0bmg6OTU9PN41RlZ07d5rKtRqLDB8+XOcXHo9HoqOj9ZyCmjdS7bSagzKOX/Izzhs9/vjj+sAg1SYEBgZKrVq1dBtr7EtPnjwpAPT3q985duxYKVOmjFx//fWSmJgotWvXlt27d8tNN92kx3+JiYl64fxsF7dUGxoWFmbK209Hfa5xjBocHFxgfsGoOPsvP+PCZWEP45jxhx9+kIyMDPF6vRIXFyfjx4+XF154ocDrcnNz5Z577pHIyEgJDAyU1q1byx9//FEgXxER2bt3rwwaNEji4uLE7XZLfHy8PgFARCQvL08eeeQRSUxM1PPdb7/9tmRlZRVY3Fq1apWkpaXpfl61i4XNqWdnZ8vYsWMlOTlZXC6XJCQkyH333VdgUfCyLG5dTiqppcuLcbh4srOzJSoqSm6++eZivf5cY7F06VIBIJ9++ulZv/diqFGjhmRmZl7uzdDONg7nqqg4XK465m9xELn8sThbVmgf/WEbrRZXOkXFrly5che1HJ1P7Fi+Lh9/aF8uFCuWowux/4cOHSohISFy5MiR896eC8GfytTQoUMlODhYIiMjLVUurgSqHFixXp7J2f6mIUOGiNfrlZycnNO+bv/+/QJAHnroIRG5fGXqTHW4Zs2a0rx580u4Rf7pSizbxVXUNhkntP2pL8jPCrHz5/1HPlYoR/6M+48uyD23iOj8LV26FLt370bv3r0v6vc899xzSElJ0TduvlSys7ML3Jto5cqVWLdunb5vkD9gHPzHlR6L/yrG1bpU7Iz3ObgYzid2LF90IfwXy9Hx48cxd+5cdO3atcj7+/5XqX1Tp04d7Nmz5z9VLvzJlVgvT/ebjPfnBHz3UpkzZw7S09NN95vL/zrg1H0uVG7vj2Xqm2++wdq1ay96PK3gSizbxeWP23Q2/suxowuH5ej8cP+R83JvANHZyM3N1TcCLUpwcDCCg4Mv0Radv6+++go//PADxo8fj1q1ahW4ueuF8tprr+GHH37AO++8g6eeesp0I9qzdS5x2Lp1KzIzM3HjjTeidOnS+OWXX/Dss8+iVKlSGDhw4Dlvy4VixTgAZx8Lf48DYN1YXEhs687d5Y6rFWJ3ttuYP3alSpW6KNt1PrH7r5Qvf5K/HKmbRaubcQOXv6yfrf9iOdq1axc+/PBDLF68GHv37sWQIUPO6/Os0AYWl9o3s2bNwp49e/DLL7/4fbm4kva/smvXLmRnZ2PIkCGoVq0aKlasaGpngAvzmy5lvSxOW9OgQQM0bdoUlStXxs6dO/HCCy/g0KFDePDBB02vW7BgAWbPno127dohODgYn3/+OebPn49WrVph8+bNuP/++/2irVHWr1+Pb7/9FpMmTUJsbCx69OhRrPddiWX7v9LnFBa7pUuXYsOGDXjnnXcwfvx4HDlyhLErxOWOnT9hG3DuXnvtNaxdu1bXt507dxb6Ou6/wrEeWsDlPnWsuHgqrX+43HEw3s+iqEf+6yH7u6ysLHE4HJKWlqZvyFfc951NLPD/13ru27evZGdnn8umaucShwMHDkj37t31tV7Dw8OlW7du8scff5zXtlwo5xqHs3WmOJxtXM82Fv4eBxH/icXZupDt48Vq6y5nG27VuJ4tK/RTZ7uN+WN3scrR+cTuv1K+/IkVyvrZsnI5Otd6qe4HEh0dbbpZ+7m6kOXico871L7xer1it9stUS6uxHqZ/z5yF+s3Xcr2vThtzX333Sfly5eXgIAACQwMlPT0dNN9Z5Vvv/1WWrRoIREREeJyuSQ+Pl6GDBki//7772Xvswqrw6NHjxabzSaVKlWSlStXFvuzrsSybeU+52ycS+z8/bKEVoqdP+6/c8E24NwBMN3flPvv7FzuNpTOzCYiAiKLOH78OD7//PPTviYlJQUpKSmXaIv+mxgH/8FYXJkYV+uyQuyssI3k/1iOqDAsF5fXlbj/r8TfRGeP5cC6GDu6EFiOzg/3H13JuLhFRERERERERERERERElmG/3BtAREREREREREREREREVFzOy70BdOXIy8vDtm3bEBIS4nc32BMR/PvvvyhdujTs9v/umq4/xwhgnBR/jhNjdArj5P/8OUYA46T4c5wYo1MYJ//nzzECGCfFn+PEGJ3COFkD4+T//DlGAOOk+HOcGKNTGCeigri4RRfMtm3bkJCQcLk347Q2b96M+Pj4y70Zl40VYgQwTlaI0389RgDjZAVWiBHAOFkhTv/1GAGMkxVYIUYA42SFOP3XYwQwTlbBOPk/K8QIYJysEKf/eowAxomoMFzcogsmJCQEgK8hCw0NvcxbY3bo0CEkJCTobfyv8ucYAYyT4s9xYoxOYZz8nz/HCGCcFH+OE2N0CuPk//w5RgDjpPhznBijUxgna2Cc/J8/xwhgnBR/jhNjdArjRFQQF7foglGnxIaGhvpdI6v422m7l5oVYgQwTlaI0389RgDjZAVWiBHAOFkhTv/1GAGMkxVYIUYA42SFOP3XYwQwTlbBOPk/K8QIYJysEKf/eowAxomoMLwIJhEREREREREREREREVkGF7eIiIiIiIiIiIiIiIjIMri4RURERERERERERERERJbBxS0iIiIiIiIiIiIiIiKyDC5uERERERERERERERERkWVwcYuIiIiIiIiIiIiIiIgsg4tbREREREREREREREREZBlc3CIiIiIiIiIiIiIiIiLL4OIWERERERERERERERERWQYXt4iIiIiIiIiIiIiIiMgyuLhFREREREREREREREREluG83BtAF96nn36KiRMn4ttvv8X27dvxv//9D507dz7te1auXIm77roLP/30ExISEjBy5Ej06dPngm5X06ZNC/1ef/m8S41xsob/QpwYI/+P0cX4vEvtvxInq2OcrOG/ECerx/y/EKOL8XmXGuNkDf+FODFG/h+ji/F5lxrjZA3/hThZPUaA/8apKKfb50X97Wzek5OTc34bSHSOeObWFejIkSOoUaMGpk+fXqzXb9q0Ce3bt0ezZs2wdu1aDB06FLfccgs++OCDC7ZNhTWIp3v+Un/e5cA4WcOVHifGyP9jdDE+73L4L8TpSsA4WcOVHqcrIeZXeowuxuddDoyTNVzpcWKM/D9GF+PzLgfGyRqu9DhdCTEC/DNORTndPj/beJzuPUSXg01E5HJvBF08NpvtjEcP3HPPPXjnnXewfv16/VzPnj1x4MABvP/++8X+rkOHDiEsLAwHDx5EaGiofr44jd7ZHKFxLp9X1Lb5i0sVp9PtB8bpzK60ODFGbPMulyutLp0rxsnHn+Pk7zECrrw4sc3z/xid6+cxTj7+HCd/jxFw5cWJdYn5+OVypdWlc/08xsmHbd758Yc4FeVSLUTl5OTgiy++8Os40ZWJZ24RvvzyS2RmZpqea926Nb788svz/uz8jejKlSv143Svu1SfZyWMkzVYJU6Mkf/H6GJ8npVYKU7/ZYyTNVglTv/lmFslRhfj86yEcbIGq8SJMfL/GF2Mz7MSxskarBKn/3KMgIsbp6IUd58b/1aYM72nqPcRXQq85xZhx44diImJMT0XExODQ4cO4dixYwgICCj0fSdOnMCJEyf0vw8dOnTa78nf2K0s4vqtxXWhP8/fnUuczjZGAON0vqwYJ8aIbZ4/8ve6tHPnThw8eFB/744dO874nlKlSsHj8QAAwsLCiixrVnKlxelKjBHg/3G62J9lBVaM0cX4PH/HOFmDFePEGDEf90dWrEsX4/P8nRXj9F+LEXBh4/TTTz/h8OHD+vlSpUohOjoaJ06cwK+//lro91/sBah3330XYWFhF/U7iArDxS06Z48++ijGjh17uTeDToMxsgbGyRoYJ/93qWK0c+dO3NirN7JPnjjzi4vgcnsw45niXZ/9SmOVOP2XYwSwzbMCxsgaGCdrYJysgXHyf4yRNTBO1lBUnEaMGAGn0zyl73S5AclDTk7Opdo8Ir/AyxISSpUqhZ07d5qe27lzJ0JDQ097xPJ9992HgwcP6sfmzZsv9qb+p51LnBijS49x8n9s86zBn+vSwYMHkX3yBI6lZOBIlY44UqkdjiU3OePjSKV2OFKlI46lZCD75IliHSHp7660OF2JMQL8O07kwxhZA+NkDYyT/2M+bg2sS9bAOFnDhYzT8TINzOOYpHTkZJ9ETk4OjsfV1s/nXvRfRXT5cXGL0KBBA6xYscL03PLly9GgQYPTvs/j8SA0NNT0OJ38pxif7ynHF/rz/N25xOlsYwQwTufLinFijNjm+SMr1KU8bxjygiKRF1IKOZHlzvjICynle733yrlcw5UWpysxRoA14nQxP8sKrBiji/F5/o5xsgYrxokxYj7uj6xYly7G5/k7K8bpvxYj4MLGKS8w3DyGCSyp35MbFq+fP274rIu9j9u1a3dRP5+oKLws4RXo8OHD+OOPP/S/N23ahLVr16JkyZIoU6YM7rvvPmzduhWvvPIKAGDgwIGYNm0a7r77btx888346KOPsHDhQrzzzjvnvS35r5tbVGNa3Gu/XujPu5wYJ8bpbF3I/coY+X+MLsbnXU5XcpyuJIyTNVypcbqSYn6lxuhifN7lxDgxTmeLbV7hrtQYXYzPu5wYJ8bpbLHNK5o/xalIVToCG97U/zzdAte5/O2/sChJ/o9nbl2BvvnmG9SqVQu1atUCANx1112oVasWRo0aBQDYvn07/vnnH/365ORkvPPOO1i+fDlq1KiBSZMm4fnnn0fr1q0vyPacqWM6247rQn/e5cI4nd3nXS5XcpwYI/+P0cX4vMvlSo/TlYJxsoYrOU5XSsyv5BhdjM+7XBins/u8y+VKjhNj5P8xuhifd7kwTmf3eZfLlRynKyVGgP/Fiei/yiYicrk3gq4Mhw4dQlhYGA4ePFjoKc2FreifT8d1Np93pm37ryjOfmCcLr9LHSfG6NywzfN/F6su/fbbb+jfvz+OVOmIvKDIs94u+5E9CNrwJiZPnoy0tDTGyQ/jxBgVxL7J/zHPswZ/jhNjdArbPGtgPu7//LnNK+72/RewzbMGtS9q9X0YtvB4/bwauwAwjX3U87NmzUL//v0LfJ7a50XF43Rxyv+3nJwcfPHFF4wTXXK8LCFdMhf6CAwrHdFhJYyTNVzI/coYXRysS9bA/WoNjJM1sG/yf+ybrIFxsga2ef6PdckaGCdrYJtnbafb50X97WzeoxbeiC41XpaQiIiIiIiIiIiIiIiILIOLW0RERERERERERERERGQZXNwiIiIiIiIiIiIiIiIiy+DiFhEREREREREREREREVkGF7eIiIiIiIiIiIiIiIjIMri4RURERERERERERERERJbBxS0iIiIiIiIiIiIiIiKyDC5uERERERERERERERERkWVwcYuIiIiIiIiIiIiIiIgsg4tbREREREREREREREREZBlc3CIiIiIiIiIiIiIiIiLL4OIWERERERERERERERERWQYXt4iIiIiIiIiIiIiIiMgyuLhFRERERERERERERERElsHFLSIiIiIiIiIiIiIiIrIMLm4RERERERERERERERGRZXBxi4iIiIiIiIiIiIiIiCyDi1tERERERERERERERERkGVzcIiIiIiIiIiIiIiIiIsvg4hYRERERERERERERERFZBhe3iIiIiIiIiIiIiIiIyDK4uEVERERERERERERERESWwcUtIiIiIiIiIiIiIiIisgwubhEREREREREREREREZFlcHGLiIiIiIiIiIiIiIiILIOLW0RERERERERERERERGQZXNwiIiIiIiIiIiIiIiIiy+DiFhEREREREREREREREVkGF7eIiIiIiIiIiIiIiIjIMri4RURERERERERERERERJbBxS0iIiIiIiIiIiIiIiKyDC5uERERERERERERERERkWVwcYuIiIiIiIiIiIiIiIgsg4tbREREREREREREREREZBlc3CIiIiIiIiIiIiIiIiLL4OIWERERERERERERERERWQYXt4iIiIiIiIiIiIiIiMgyuLhFRERERERERERERERElsHFLSIiIiIiIiIiIiIiIrIMLm4RERERERERERERERGRZXBxi4iIiIiIiIiIiIiIiCyDi1tERERERERERERERERkGVzcIiIiIiIiIiIiIiIiIsvg4hYRERERERERERERERFZBhe3iIiIiIiIiIiIiIiIyDK4uEVERERERERERERERESWwcUtIiIiIiIiIiIiIiIisgwubhEREREREREREREREZFlcHGLiIiIiIiIiIiIiIiILIOLW0RERERERERERERERGQZXNwiIiIiIiIiIiIiIiIiy+DiFhEREREREREREREREVkGF7eIiIiIiIiIiIiIiIjIMri4RURERERERERERERERJbBxS0iIiIiIiIiIiIiIiKyDC5uERERERERERERERERkWVwcYuIiIiIiIiIiIiIiIgsg4tbREREREREREREREREZBlc3CIiIiIiIiIiIiIiIiLL4OIWERERERERERERERERWQYXt4iIiIiIiIiIiIiIiMgyuLhFRERERERERERERERElsHFrSvY9OnTkZSUBK/Xi3r16uHrr78+7euffPJJVKxYEQEBAUhISMCdd96J48ePX6Kt/W9ijKyBcbIGxskaGCf/xxhZA+NkDYyT/2OMrIFxsgbGyf8xRtbAOFkD40R0+XFx6wq1YMEC3HXXXRg9ejS+++471KhRA61bt8auXbsKff2rr76Ke++9F6NHj8bPP/+MF154AQsWLMD9999/ibf8v4MxsgbGyRoYJ2tgnPwfY2QNjJM1ME7+jzGyBsbJGhgn/8cYWQPjZA2ME5F/4OLWFWry5Mno168fbrrpJlSpUgXPPvssAgMD8eKLLxb6+lWrVqFRo0a4/vrrkZSUhFatWuG6664741EHdO4YI2tgnKyBcbIGxsn/MUbWwDhZA+Pk/xgja2CcrIFx8n+MkTUwTtbAOBH5By5uXYFOnjyJb7/9FpmZmfo5u92OzMxMfPnll4W+p2HDhvj22291o/rnn3/i3XffRbt27Yr8nhMnTuDQoUOmBxUPY2QNjJM1ME7WcCnixBidH9Yla2CcrIFtnv9jXbIGxskaGCf/xxhZA+NkDYwTkf9wXu4NoAtvz549yM3NRUxMjOn5mJgY/PLLL4W+5/rrr8eePXuQnp4OEUFOTg4GDhx42tNjH330UYwdO/aCbvt/BWNkDYyTNTBO1nAp4sQYnR/WJWtgnKyBbZ7/Y12yBsbJGhgn/8cYWQPjZA2ME5H/4JlbBABYuXIlHnnkETzzzDP47rvvsGTJErzzzjsYP358ke+57777cPDgQf3YvHnzJdzi/x7GyBoYJ2tgnKzhbOPEGF16rEvWwDhZA9s8/8e6ZA2MkzUwTv6PMbIGxskaGCeii4Nnbl2BIiMj4XA4sHPnTtPzO3fuRKlSpQp9z4MPPohevXrhlltuAQCkpqbiyJEj6N+/Px544AHY7QXXQT0eDzwez4X/Af8BjJE1ME7WwDhZw6WIE2N0fliXrIFxsga2ef6PdckaGCdrYJz8H2NkDYyTNTBORP6DZ25dgdxuN9LS0rBixQr9XF5eHlasWIEGDRoU+p6jR48WaEgdDgcAQEQu3sb+RzFG1sA4WQPjZA2Mk/9jjKyBcbIGxsn/MUbWwDhZA+Pk/xgja2CcrIFxIvIfPHPrCnXXXXchKysLderUwVVXXYUnn3wSR44cwU033QQA6N27N+Li4vDoo48CADp06IDJkyejVq1aqFevHv744w88+OCD6NChg25s6cJijKyBcbIGxskaGCf/xxhZA+NkDYyT/2OMrIFxsgbGyf8xRtbAOFkD40TkH7i4dYXq0aMHdu/ejVGjRmHHjh2oWbMm3n//fX2zw3/++cd0xMDIkSNhs9kwcuRIbN26FVFRUejQoQMefvjhy/UTrniMkTUwTtbAOFkD4+T/GCNrYJysgXHyf4yRNTBO1sA4+T/GyBoYJ2tgnIj8g0147iNdIIcOHUJYWBgOHjyI0NDQy705Jv68bZeSv+8Hf9++S8Wf94M/b9ul5s/7wp+37VK6WPvht99+Q//+/XGkSkfkBUWe9fvtR/YgaMObmDx5MtLS0hgnP4wTY1SQP7cr/rxtl5K/7wd/375LxZ/3gz9v26Xmz/vCn7ftUvPnfeHP23Yp+ft+8Pftu1T8eT/487Zdampf1Or7MGzh8fp5NXYBYBr7qOdnzZqFChUqXJJtY5zoUuM9t4iIiIiIiIiIiIiIiMgyuLhFRERERERERERERERElsHFLSIiIiIiIiIiIiIiIrIMLm4RERERERERERERERGRZXBxi4iIiIiIiIiIiIiIiCyDi1tERERERERERERERERkGVzcIiIiIiIiIiIiIiIiIsvg4hYRERERERERERERERFZBhe3iIiIiIiIiIiIiIiIyDK4uEVERERERERERERERESWwcUtIiIiIiIiIiIiIiIisgwubhEREREREREREREREZFlcHGLiIiIiIiIiIiIiIiILIOLW0RERERERERERERERGQZXNwiIiIiIiIiIiIiIiIiy+DiFhEREREREREREREREVkGF7eIiIiIiIiIiIiIiIjIMri4RURERERERERERERERJbBxS0iIiIiIiIiIiIiIiKyDC5uERERERERERERERERkWVwcYuIiIiIiIiIiIiIiIgsg4tbREREREREREREREREZBlc3CIiIiIiIiIiIiIiIiLL4OIWERERERERERERERERWQYXt4iIiIiIiIiIiIiIiMgyuLhFRERERERERERERERElsHFLSIiIiIiIiIiIiIiIrIMLm4RERERERERERERERGRZXBxi4iIiIiIiIiIiIiIiCyDi1tERERERERERERERERkGVzcIiIiIiIiIiIiIiIiIsvg4hYRERERERERERERERFZBhe3iIiIiIiIiIiIiIiIyDK4uEVERERERERERERERESWwcUtIiIiIiIiIiIiIiIisgwubhEREREREREREREREZFlcHGLiIiIiIiIiIiIiIiILIOLW0RERERERERERERERGQZXNwiIiIiIiIiIiIiIiIiy+DiFhEREREREREREREREVkGF7eIiIiIiIiIiIiIiIjIMri4RURERERERERERERERJbBxS0iIiIiIiIiIiIiIiKyDC5uERERERERERERERERkWVwcYuIiIiIiIiIiIiIiIgsg4tbREREREREREREREREZBlc3CIiIiIiIiIiIiIiIiLL4OIWERERERERERERERERWQYXt4iIiIiIiIiIiIiIiMgyuLhFRERERERERERERERElsHFLT/z8ccfX+5NoDNgjKyBcbIGxsn/MUbWwDhZA+NkDYyT/2OMrIFxsgbGyf8xRtbAOFkD40R0ZeHilp9p06YNypYti4ceegibN2++3JtDhWCMrIFxsgbGyf8xRtbAOFkD42QNjJP/Y4ysgXGyBsbJ/zFG1sA4WQPjRHRl4eKWn9m6dSsGDRqExYsXIyUlBa1bt8bChQtx8uTJy71p9P8YI2tgnKyBcfJ/jJE1ME7WwDhZA+Pk/xgja2CcrIFx8n+MkTUwTtbAOBFdWbi45WciIyNx5513Yu3atfjqq69QoUIF3HbbbShdujTuuOMOrFu37nJv4n8eY2QNjJM1ME7+jzGyBsbJGhgna2Cc/B9jZA2MkzUwTv6PMbIGxskaGCei/2vvvsOjKvP/jb/TE0oA6SUQiqFID0pTEQVjWRQbCAiIIKsiFvxaWF2wrIsVUURZWMGyiqyCiqAooigiCgIBlBJQmkuRolQFTD6/P/hlZEhCAoTM8wz367q4LnJm5swz555zZibPTCa8MLnlsObNm2vw4MG69dZbtWfPHo0bN06pqak655xz9P3334d6eBCNfEEnP9DJfTTyA538QCc/0Ml9NPIDnfxAJ/fRyA908gOdAP8xueWggwcP6u2339Yll1yiGjVq6KOPPtLzzz+vLVu2aPXq1apRo4auueaaUA/zlEYjP9DJD3RyH438QCc/0MkPdHIfjfxAJz/QyX008gOd/EAnIHxEh3oACDZw4EBNmDBBZqaePXvqiSeeUMOGDQOnFy9eXE899ZSqVKkSwlGe2mjkBzr5gU7uo5Ef6OQHOvmBTu6jkR/o5Ac6uY9GfqCTH+gEhBcmtxyzbNkyjRw5UldeeaXi4uJyPU+5cuX02WefFfHIkI1GfqCTH+jkPhr5gU5+oJMf6OQ+GvmBTn6gk/to5Ac6+YFOQHjhzxI65ODBg6pRo4ZatWqV5wFWkqKjo9WuXbsiHBmy0cgPdPIDndxHIz/QyQ908gOd3EcjP9DJD3RyH438QCc/0AkIP0xuOSQmJkaTJk0K9TBwFDTyA538QCf30cgPdPIDnfxAJ/fRyA908gOd3EcjP9DJD3QCwg+TW47p3Lmz3n333VAPA0dBIz/QyQ90ch+N/EAnP9DJD3RyH438QCc/0Ml9NPIDnfxAJyC88J1bjjn99NP18MMPa86cOUpNTVXx4sWDTr/ttttCNDJko5Ef6OQHOrmPRn6gkx/o5Ac6uY9GfqCTH+jkPhr5gU5+oBMQXiLMzEI9CPypZs2aeZ4WERGhH3/8scDrGjVqlJ588klt3rxZTZo00ciRI3XWWWflef5ff/1V999/vyZPnqwdO3aoRo0aGjFihC655JICXd+uXbtUqlQp7dy5U4mJiQUeZ1EozLHR6OSh0yEudyrssdHp5HB1X5KKttPJapSRkaH+/ftrb4PLlFW83DFfPnLvNhVfNkXDhw9Xamqqc53CZV86kU4no5FEp5OFx6ZDXG4kufvYxL70J/alP9HpEDodP455h7jcSKJTNpc7ccz7U/a2aNb3UUWUqRZYnv3aRVLQa5/s5WPGjFFKSkqBb9fxcPk+hPDGJ7ccs2bNmkJZz8SJEzVo0CCNHj1aLVu21IgRI5SWlqaVK1eqQoUKOc5/4MABdezYURUqVNDbb7+tqlWrat26dSpdunShjCec0MgPdPIDndxXWI0kOp1M7Et+oJMf6OQ+GvmBTn6gk/to5Ac6+YFOQHhhcitMDR8+XDfeeKP69OkjSRo9erSmTZumcePG6b777stx/nHjxmnHjh366quvFBMTI0lKTk4uyiGfcmjkBzr5gU5+oJP7aOQHOvmBTu6jkR/o5Ac6uY9GfqCTH+gEuIHJLQf99NNPmjJlitavX68DBw4EnTZ8+PB8L3/gwAEtWLBAgwcPDiyLjIxUhw4dNHfu3FwvM2XKFLVu3VoDBgzQe++9p/Lly6t79+669957FRUVdWI3KAzRyA908gOd3HeijSQ6FQX2JT/QyQ90ch+N/EAnP9DJfTTyA538QCcgfDC55ZiZM2fqsssuU61atbRixQo1bNhQa9eulZmpefPmBVrHtm3blJmZqYoVKwYtr1ixolasWJHrZX788Ud9+umn6tGjhz744AOtXr1at9xyiw4ePKihQ4fmepn9+/dr//79gZ937dpVwFvpNxr5gU5+oJP7CqORVDSdTtVGEvuSL+jkB1860cj9RhKd6OQ+OrmPRn6gkx/oBISXyFAPAMEGDx6s//u//9PSpUsVHx+vSZMmacOGDWrXrp2uueaak3a9WVlZqlChgsaMGaPU1FR17dpV999/v0aPHp3nZYYNG6ZSpUoF/iUlJZ208bmERn6gkx/o5L5QNZKOvdOp2khiX/IFnfzgSycaud9IohOd3Ecn99HID3TyA52A8MLklmOWL1+uXr16SZKio6P122+/qUSJEnr44Yf1+OOPF2gd5cqVU1RUlLZs2RK0fMuWLapUqVKul6lcubJSUlKCPgpbv359bd68OcdHdLMNHjxYO3fuDPzbsGFDgcbnOxr5gU5+oJP7CqORVDSdTtVGEvuSL+jkB1860cj9RhKd6OQ+OrmPRn6gkx/oBIQXJrccU7x48cBBrXLlyvrhhx8Cp23btq1A64iNjVVqaqpmzpwZWJaVlaWZM2eqdevWuV6mbdu2Wr16tbKysgLLMjIyVLlyZcXGxuZ6mbi4OCUmJgb9OxXQyA908gOd3FcYjaSi6XSqNpLYl3xBJz/40olG7jeS6EQn99HJfTTyA538QCcgvDC55ZhWrVrpyy+/lCRdcskluuuuu/Too4/qhhtuUKtWrQq8nkGDBmns2LF65ZVXtHz5ct18883au3ev+vTpI0nq1atX0Bcf3nzzzdqxY4duv/12ZWRkaNq0afrnP/+pAQMGFO4NDAM08gOd/EAn9xVWI4lOJxP7kh/o5Ac6uY9GfqCTH+jkPhr5gU5+oBMQXqJDPQAEGz58uPbs2SNJeuihh7Rnzx5NnDhRp59+uoYPH17g9XTt2lVbt27VkCFDtHnzZjVt2lTTp08PfNnh+vXrFRn559xmUlKSPvroI915551q3Lixqlatqttvv1333ntv4d7AMEAjP9DJD3RyX2E1kuh0MrEv+YFOfqCT+2jkBzr5gU7uo5Ef6OQHOgHhJcLMLNSDQHjYtWuXSpUqpZ07dzr3UVmXx1aUXN8Oro+vqLi8HVweW1FzeVu4PLaidLK2Q0ZGhvr376+9DS5TVvFyx3z5yL3bVHzZFA0fPlypqal0crATjXJy+bji8tiKkuvbwfXxFRWXt4PLYytqLm8Ll8d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",
      "text/plain": [
       "<Figure size 1500x500 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 假设 data 是你的数据框，discrete_vars 是离散变量列表\n",
    "discrete_vars = ['HighBP', 'HighChol', 'CholCheck', 'Smoker', 'Stroke', 'HeartDiseaseorAttack', 'PhysActivity', 'Fruits','Veggies' , 'HvyAlcoholConsump' ,'AnyHealthcare' ,'NoDocbcCost' ,'Sex' ,'Education']  # 替换为实际的离散变量列名\n",
    "fig, axes = plt.subplots(1, len(discrete_vars), figsize=(15, 5))\n",
    "\n",
    "for i, var in enumerate(discrete_vars):\n",
    "    sns.boxplot(data=data, x=var, y='Diabetes_binary', ax=axes[i])\n",
    "    axes[i].set_title(f'Boxplot of Diabetes_binary by {var}')\n",
    "    axes[i].set_xlabel(var)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 热力图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入相应包\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "corr_df = data[['BMI','Age','MentHlth','PhysHlth','GenHlth']].corr()\n",
    "mask = np.array(corr_df)\n",
    "mask[np.tril_indices_from(mask)] = False  # mask = np.zeros_like(corr) # mask[np.triu_indices_from(mask)] = True\n",
    "plt.figure(figsize=(20, 10))\n",
    "sns.heatmap(corr_df, mask=mask, vmax=0.8, square=True, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 经观察，发现有些特征对于标签的影响并不大，甚至几乎没有影响，可以通过降维提取重要特征来减少数据的噪声和冗余，提升模型的训练速度，降低特征的共线性等，最终达到提升模型预测精度，模型泛化性的作用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98939     0.0\n",
       "209426    0.0\n",
       "48298     0.0\n",
       "153179    0.0\n",
       "191760    0.0\n",
       "         ... \n",
       "215275    0.0\n",
       "190420    1.0\n",
       "192084    0.0\n",
       "74335     0.0\n",
       "178903    0.0\n",
       "Name: Diabetes_binary, Length: 253680, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder # 用于特征标准化、独热编码和数值编码\n",
    "from sklearn.model_selection import train_test_split # 用于将数据集划分为训练集和测试集\n",
    "\n",
    "\n",
    "# 分离特征数据和标签数据\n",
    "X = data.drop(['Diabetes_binary'], axis=1)  # 特征数据\n",
    "y = data['Diabetes_binary']  # 标签数据\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 进行降维处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "每个主成分的方差解释比例: [0.16713006 0.08396704 0.06480288 0.05667649 0.05512874 0.05176754\n",
      " 0.04916833 0.04515407 0.04399236 0.04190459 0.03864939 0.03771927\n",
      " 0.03557995 0.03488982 0.03391184]\n",
      "降维后的数据形状: (253680, 15)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    " #数据标准化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "#创建 PCA 模型，指定降维后的维度\n",
    "# 将维度降至 15\n",
    "n_components = 15\n",
    "pca = PCA(n_components=n_components)\n",
    "\n",
    "# 步骤 4: 拟合和转换数据\n",
    "X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "# 步骤 5: 查看每个主成分的方差解释比例\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "print(f\"每个主成分的方差解释比例: {explained_variance_ratio}\")\n",
    "print(f\"降维后的数据形状: {X_pca.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型准确率: 0.8609862819299905\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.model_selection import train_test_split # 用于将数据集划分为训练集和测试集\n",
    "from sklearn.linear_model import LogisticRegression  # 逻辑回归模型\n",
    "# 划分数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 创建逻辑回归模型\n",
    "model = LogisticRegression()\n",
    "\n",
    "# 模型训练\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 模型预测\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# 评估模型\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"模型准确率: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC  # 支持向量机分类模型\n",
    "# 定义 SVM 模型（使用默认参数）\n",
    "svm = SVC(probability=True, random_state=42)  # 启用概率估计\n",
    "\n",
    "# 训练模型\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred_proba = svm.predict_proba(X_test)[:, 1]  # 获取正类的概率\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"模型准确率: {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 28125, number of negative: 174819\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006309 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 3825\n",
      "[LightGBM] [Info] Number of data points in the train set: 202944, number of used features: 15\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.138585 -> initscore=-1.827092\n",
      "[LightGBM] [Info] Start training from score -1.827092\n",
      "测试集 AUC:  0.818955948364056\n"
     ]
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "\n",
    "# 定义 LightGBM 模型（使用默认参数）\n",
    "lgb_model = lgb.LGBMClassifier(random_state=42)\n",
    "\n",
    "# 训练模型\n",
    "lgb_model.fit(X_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred_proba = lgb_model.predict_proba(X_test)[:, 1]  # 获取正类的概率\n",
    "\n",
    "# 输出测试集 AUC\n",
    "print(\"测试集 AUC: \", roc_auc_score(y_test, y_pred_proba))\n",
    "# y_pred_proba"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " AUC:  0.7941630150662836\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier  # 随机森林分类模型\n",
    "# 定义随机森林模型（使用默认参数）\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# 训练模型\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred_proba = rf.predict_proba(X_test)[:, 1]  # 获取正类的概率\n",
    "\n",
    "# 输出测试集 AUC\n",
    "print(\" AUC: \", roc_auc_score(y_test, y_pred_proba))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集 AUC:  0.8146737265838784\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb  # XGBoost模型\n",
    "# 定义 XGBoost 模型（使用默认参数）\n",
    "xgb_model = xgb.XGBClassifier(random_state=42)\n",
    "\n",
    "# 训练模型\n",
    "xgb_model.fit(X_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred_proba = xgb_model.predict_proba(X_test)[:, 1]  # 获取正类的概率\n",
    "\n",
    "# 输出测试集 AUC\n",
    "print(\"测试集 AUC: \", roc_auc_score(y_test, y_pred_proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes:\n",
      "AUC:  0.776962014874747\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "# 朴素贝叶斯\n",
    "print(\"Naive Bayes:\")\n",
    "nb = GaussianNB()\n",
    "nb.fit(X_train, y_train)\n",
    "y_pred_nb = nb.predict_proba(X_test)[:, 1]\n",
    "print(\"AUC: \", roc_auc_score(y_test, y_pred_nb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **未降维模型初步评估结果**\n",
    "\n",
    "| 模型名称               | 测试集 auc   |                   \n",
    "|------------------------|---------------|\n",
    "| 逻辑回归               | 0.7218283396396269|\n",
    "| svm支持向量机         | 0.7591634615384615 |\n",
    "| 朴素贝叶斯           | 0.7701783006377199     |\n",
    "| 随机森林               | 0.7679394538536416    |\n",
    "| XGBoot                | 0.8252390337698228     |\n",
    "| LightGBM              | 0.7998262246687106    |\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "**说明**\n",
    " **auc**：模型预测准确率，值越接近1表示模型性能越好。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **降维后模型初步评估结果**\n",
    "\n",
    "| 模型名称               | 测试集 auc   |\n",
    "|------------------------|---------------|\n",
    "| 逻辑回归               | 0.8609862819299905|\n",
    "| svm支持向量机         | 0.7991634615384615 |\n",
    "| 朴素贝叶斯           | 0.776962014874747      |\n",
    "| 随机森林               | 0.7941630150662836     |\n",
    "| XGBoot                | 0.8146737265838784    |\n",
    "| LightGBM              | 0.818955948364056  |\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "**说明**\n",
    " **auc**：模型预测准确率，值越接近1表示模型性能越好。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 经对比，通过降维训练默认参数下的模型在预测精度基本上都得到了提升"
   ]
  }
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